esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology and Data
### Qualitative Interview
#### 3.1.2 Data Collection Process  
Before the interview, the interviewer collected the background information of all the selected SMEs. This information included the company's establishment date, main industry sector, business type, capital, business products or services, the branch stores or offices, etc. Most of the information was obtained from the company's website or government agencies' databases. All the collected information was brought by interviewers to the interview site and confirmed by the interviewees. Before the formal interview, the researchers prepared the questions derived from previous literature cited in this study. A pre-interview on three SMEs was conducted and generates successful results.  
The formal interview phase was conducted in a face-to-face manner. The interviews took place in the meeting room of respondents' companies or in any easy-access location for the respondents. The duration of the interview was approximately an hour. Half of the interviews lasted approximately 1.5 h. There was a total of 12 interviews for 12 business respondents.  
The interview was divided into two parts. In the first part, each company's basic information was verified with respondent, and the missing information in the pre-collected part was collected. This information included the number of employees and business branches. The second part was based on a pre-prepared questions list. The questions list collects important information from the respondents, regarding possible determinants in technology, organization, environment, and internal users' contact. The information indicated whether there were either accelerators or obstacles in adopting broadband mobile applications within the enterprise.  
The interview was live-recorded and notes were made by two researchers during the interview. The manuscripts were compared and sent to be confirmed by the respondents within 24 h after the conclusion of the interview. In the process of data collecting and analysis after the interview, as [PERSON] [24] pointed out, a determinant is considered important if the respondent considers the determinant has an impact on the adoption and implementation of the technology.
# Situational Awareness: Mapping Interference Sources in Real-Time Using a Smartphone App
## 6 On-Field Measurement Campaigns
### Interferences in an Urban Scenario  
Three examples of non-harmful interference detected during real-life urban situations are reported in the following case-studies.
## 3 Routing
### Routing in CRSN: Rising Issues
#### 3.2.5 Intermittent Connectivity  
Upon the arrival of the primary user, the sensor node must vacate the current channel and perform spectrum handoff. This process introduces added delays and has the potential of increasing loss rates in the network because the route to the sink is constantly changing or interrupted.
## 3 Analysis of Test Results
### Response Surface Analysis  
Employing the response surface method, the influence of various factors on the response outcomes within a defined range is mirrored through the deformation of the response surface and contour trends. When multiple factors concurrently affect the test results, and these factors exhibit interactive effects, this methodology effectively obtains their impacts [55]. In this section, the response surface is utilized to visualize the effect of the interplay between two factors on the ultimate outcome amidst multiple influencing factors in the experimental setting. It is necessary to substitute the mean values of the remaining factors' levels other than the factors to be analyzed into the objective equation, effectively minimizing the independent variables [34; 42; 56]. For example, Equation (8) is obtained by substituting into Equation (2) the average values from Table 6 for all variables except \(x_{1}\) and \(x_{4}\), which are the variables under analysis.
# Connecting the Brain with Augmented Reality: A Systematic Review of BCI-AR Systems
## 4 Results
### Reactive BCI
#### 4.1.5 Signal Processing and Feature Extraction  
The majority of the researchers applied a variety of bandpass filters: 1-35 Hz [14]; 2-54 Hz [15; 29]; 0.5-45 Hz [19]; 1-20 Hz [21]; 7-90 Hz [24]; 0.1-50 Hz [28]; 7-17 Hz, 16-32 Hz, and 25-47 Hz [30]; 5-25 Hz [31; 32; 40]; 0.5-100 Hz [33] and 9-25 Hz [35]; 5-20 Hz [36]; 0.5-50 Hz [37]; 0.1-12 Hz [38]; 5-40 Hz [39]; 5-90 Hz [41]; 0.5-60 Hz [42]; 8-40 Hz [44]; and 0.5-30 Hz [45]. The researchers in [20] applied a highpass, a lowpass, and a notch filter,while those in [26] applied a highpass filter above 0.5 Hz and two lowpass filters below 50 and 12 Hz, respectively. Also, Refs. [20; 22; 24] applied a notch filter to remove the powerline noise. The authors of [19; 23] calculated the PSD using FFT, while those of [25] applied only FFT. The CSP technique was used by [17].  
To extract the desired features for the classifier, the vast majority of the researchers [14; 32; 33; 35; 41] used CCA. FFT was also employed by [18; 23; 32; 42], while stepwise LDA was used by [37]. Finally, ref. [44] employed extended CCA and FFT for feature extraction.
## 9 Conclusions  
The reliability of power supply to consumers largely depends on operating decisions taken to withstand anticipated and random generation and load fluctuations. The concerns with growing uncertainty in power system conditions can be addressed using a probabilistic approach that can quantify the degree of operating risk and be used in decision making. The CGRR index proposed in this paper is responsive to the uncertainty of varying magnitudes of power disturbances, the amount of operating reserve in the system, ramp rates and responding capacity of the units carrying the reserve, and the lead time, after which the system will receive further capacity assistance. The index therefore provides a comprehensive risk assessment that should be useful in operating decisions with unit commitment and load dispatch. The paper presents an analytical method to evaluate the index, and an application of the method is illustrated on a test system. The paper also presents a Monte Carlo simulation method for CGRR evaluation which was then used to validate the analytical method. A suitable CGRR criterion can be evaluated based on past acceptable performance, as illustrated in the paper. The criterion can then be used to operate the system at a consistent level of risk. The results show that limiting the number of contingencies to the fourth order in the analytical method can significantly reduce the computation burden without sacrificing reasonable accuracy, and therefore, be used in real-time CGRR evaluation during system operation. Offline evaluation results from all potential operating conditions will provide valuable inputs to real-time evaluation and decisions. An increase in operating reliability often requires additional operating costs. The proposed methodology and index can quantify the operating reliability benefit and be compared with the operating cost to provide useful indicators to value-based operating decisions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Committed Units and Dispatch** & **Total RM (MW)** & **CGRR \(\times\) 10\({}^{6}\)** & **Cost (5/h)** \\ \hline
13 units, ELD & 230 & 4062.161706 & 19,505.11 \\
13 units, re-dispatch & 396 & 2740.451292 & 21,118.19 \\
14 units, ELD & 355 & 2742.428945 & 19,582.40 \\
14 units re-dispatch & 401 & 57.65131996 & 19,755.09 \\
15 units ELD & 442 & 60.95279158 & 20,150.68 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Example results for operating reliability and cost comparison.  
**Funding:** The authors acknowledge the financial support from the Natural Sciences and Engineering Research Council of Canada in funding this research.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 6 Future Research Directions
### Real-Time Video Analytics  
Using a range of precise analytical and approximative numerical methodologies, intelligent video analytics is the automated analysis of images coming from video cameras in real-time or from archive recordings. Video analytics are implemented utilizing software (software) when dealing with video footage. The application makes use of a wide range of statistical models and techniques to enable video monitoring and data mining without the direct involvement of people. More and more, problems with video surveillance systems are being solved using video analytics. Video analyzing software could use to acquire an objective assessment of the performance of airspace monitoring, since it can perform continuous and automatic gathering and analysis of video data, independent of the human factor, and create reports at the user's request at any time [35; 36].
# Cable Temperature Prediction Based on RF-GPR for Digital Twin Applications
## 3 RF-GPR Prediction Model of Cable Temperature
### Characteristic Variable Importance Score Calculation  
#### 3.2.1 Random Forest Algorithm Variable Importance Score  
The random forest algorithm can be used to evaluate the importance of a set of feature data in the process of regression prediction, and its results can be used as the basis for the selection of feature variables. The conventional random forest variable importance score (VIM) calculation method can be obtained by calculating the \(Gini\) index [23]. Assuming that there are characteristic variables \(X_{1},\ X_{2},\ \cdots,\ X_{M}\), then the importance score of variable \(X_{j}\) can be calculated, and the specific formula is shown as follows.  
In a decision tree, the \(Gini\) index of node \(m\) is calculated, as shown in Equation (7).  
\[G_{m}=\sum_{k=1}^{K}P_{mk}(1-P_{mk}) \tag{7}\]  
where \(G_{m}\) is the \(Gini\) index of node \(m\), \(K\) is the number of sample categories in the total sample, and \(P_{mk}\) is the probability estimate of the sample belonging to class \(k\) at node \(m\).  
The importance of variable \(X_{j}\) in node \(m\), that is, the calculation formula of the \(Gini\) index change before and after node \(m\) splitting, is shown in Equation (8).  
\[V_{jm}^{Gini}=G_{m}-G_{ml}-G_{mr} \tag{8}\]  
where \(V_{jm}^{Gini}\) is the importance of variable \(X_{j}\) in node \(m\), and \(G_{ml}\) and \(G_{mr}\) represent the \(Gini\) index of the left and right nodes split by node \(m\), respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable Numbers** & **Input Characteristic Variables** & **Unit** \\ \hline
1 & X coordinate of the point & m \\
2 & Y coordinate of the point & m \\
3 & \(x\)-axis coordinates of the cable core center & m \\
4 & \(y\)-axis coordinates of the cable core center & m \\
5 & Excitation current & A \\
6 & Cable core conductivity & S/m \\
7 & Relative permeability & 1 \\
8 & Thermal conductivity of insulation & W/(m\(\cdot\)K) \\
9 & Convective heat transfer coefficient & W/(m\({}^{2}\cdot\)K) \\
10 & Ambient temperature & K \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristic variables of 10-dimensional input.  
If \(M\) nodes in the \(i\) tree contain variable \(X_{j}\), then the importance calculation formula of variable \(X_{j}\) in the \(i\) tree is shown in Equation (9).  
\[V_{ij}^{Gini}=\sum_{m=1}^{M}V_{jm}^{Gini} \tag{9}\]  
where \(V_{ij}^{Gini}\) is the importance of variable \(X_{j}\) in the \(i\) tree, and \(M\) is the number of nodes in the \(i\) tree containing the variable \(X_{j}\).  
If there are \(n\) trees in the random forest, the \(Gini\) importance of variable \(X_{j}\) in the random forest is defined as the average importance of variable \(X_{j}\) in all trees in the random forest, and its calculation formula is shown in Equation (10).  
\[V_{j}^{Gini}=\frac{1}{n}\sum_{i=1}^{n}V_{ij}^{Gini} \tag{10}\]  
where \(V_{j}^{Gini}\) is the \(Gini\) importance of variable \(X_{j}\) in the random forest, and \(n\) is the number of decision trees in the random forest.
# Efficient and Secure EMR Storage and Sharing Scheme Based on Hyperledger Fabric and IPFS
## 5 Scheme Implementation
### Data Storage Phase  
To provide a clearer explanation of the sharing scheme proposed in this study, Figure 3 illustrates the entire process from the data storage stage to the data sharing stage.  
The scheme creates an attribution channel and a data channel. The attribution channel is specifically designed to record the attribution information of patient records. The data channel records details such as the digests of medical data, storage locations, and usage records of EMR. Simultaneously, the encrypted original medical data are stored in the IPFS. This design effectively segregates medical data from patient identity information, offering a dependable technical solution for safegu  
Figure 3: Timing diagram for data storage phase and data sharing phase.
## 2 Related Works
The results show that the proposed model can reduce the electricity costs for household appliances and improve computational costs. [PERSON] et al. proposed the use of a genetic algorithm (\(GA\)) to reschedule the household appliance accordingly with information on energy storage systems (\(ESS\)) and photovoltaic (\(PV\)) generation [12]. In this way, the system could choose to export, import, or store energy in order to reduce energy demands and costs. The same authors also presented the use of a Grey Wolf optimization algorithm to reduce the overall electricity cost, improve the performance of home electricity scheduling, and decreasing the effect of the uncertainty of the data. In their work [3], [PERSON] et al. [13] also proposed the use of a stochastic model in order to learn and predict solar power production, simulate the energy storage system behavior, and schedule household appliance usage. The authors of [3], proposed a novel optimization technique to reduce the energy costs that imply a reduction of the energy demands from the energy provider. The technique is the fusion of the Grey Wolf algorithms with the genetic algorithm. [PERSON] et al. presented in [14] an attempt to coordinate the switching on and off of electronic devices through a load-shifting strategy approach for users with a time slot contract, trying not to affect the habits and comforts of the user. The system allows programming that takes place the day before and, if necessary, even programming in real-time. The work compares different optimization algorithms based on the application domain, the devices in use, the limitations on the user, and the results obtained. There is the use and comparison of different optimization algorithms such as \(GA\) (genetic algorithm), BFA (bacterial foraging algorithm), and HBG (hybrid bacterial foraging). The addition of micro-plants to produce renewable energy and storage systems is not considered. [PERSON] et al. proposed an approach that solves the global optimization problem using meta-heuristic algorithms for a domestic user based on time of use (TOU) consumption [15]. A comparison is made between different algorithms: \(GA\) (genetic algorithm), BPSO (binary particle swarm optimization), and ACO (ant colony optimization algorithms). Moreover, the problem-solving strategy is approached through a multiple backpack problem (Greedy). Results show that the \(GA\) algorithm is the most efficient in comparison to the others, with the minimization of the peak-average ratio and maximization of the user's comfort level. This work proposes a framework in which consumption prediction and energy production prediction are used together with the modeling of an energy storage system (\(ESS\)) to propose optimal scheduling to the user. In addition, the optimal scheduling is based on the user's consumption preferences. The proposed framework was also compared with and without the use of meteorological information for energy production prediction. To the best of the authors' knowledge, weather information has not been considered in any other work.
# Rock Crack Types Identification by Machine Learning on the Sound Signal+
## 3 Results and Discussion  
### Identifying the Development of Tensile and Shear Cracks under Biaxial Compression Test  
#### 3.1.1. _B_axial Compression Test on a Granite Specim  
Crack development during rock failure under biaxial compressive conditions can be effectively characterized by experimental studies. In the early stage of rock failure, splitting (tensile) cracks first occur on both sides of the rock specimens. At the late stage, especially just before rock failure, shear cracks occur in the middle of the tested specimen. Considering this crack development, the method proposed in our study can be verified.  
First, a biaxial test on granite is conducted. The testing equipment used is described in Section 3.1. The cuboid granite specimen has dimensions of 100 mm (length) \(\times\) 100 mm (width) \(\times\) 200 mm (height). The loading device is shown in Figure 15. The testing process can be described as follows. First, pre-stresses: a vertical stress \(\sigma_{z}\) of 0.5 MPa and a horizontal stress \(\sigma_{x}\) of 0.25 MPa are applied to the tested specimen. Then \(\sigma_{x}\) is increased to 1 MPa at a rate of 0.05 MPa/s and held constant. \(\sigma_{z}\) is increased continually at a rate of 0.5 MPa/s until the specimen fails. The failed specimen is shown in Figure 16.  
\begin{table}
\begin{tabular}{c c} \hline \hline \(k\) **(Times)** & **Accuracy (\%)** \\ \hline
1 & 100 \\
2 & 75 \\
3 & 87.5 \\
4 & 100 \\
5 & 87.5 \\ Average accuracy & 90 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of the cross-validation of the proposed method.  
Figure 16: Failed specimen after the biaxial test.  
Figure 15: Biaxial compression test: (**a**) rock specimen and loading device, (**b**) stress state of the specimen.  
According to the change characteristics of the stress-strain curve, the pre-peak deformation can be divided into four stages, as shown in Figure 17: A: initial compaction stage, B: linear elastic stage, C: elastic-plastic transition stage, and D: plastic stage. The raw sound signal is also divided according to the corresponding four stages, as shown in Figure 18. Since the rock specimens have failed in the post-peak period, the recording of sound signals is stopped in that stage after 119.4 s. From 0 to 56.743 s, there are no sound signals. From 56.743 to 119.4 s, the sounds occur sporadically throughout the recording time.
## 7 Experiments
### Effects of Sample Frame Number  
The aforementioned experiments revealed that the model established in this study was effective in processing the pedestrian gait optical flow sequence. In particular, samples employed in these experiments were set at 25 frames per unit for training because on average, the pedestrian gait cycle was shorter than 1 s and the average gait cycle from the samples in the sample set was contained within approximately 25 frames. However, the walking speed of each sequence differed. Therefore, another objective of this study was to determine whether accuracy was hindered and enhanced when the sample contained fewer and more than one complete gait cycle, respectively.  
In this experiment, the same sample distribution method as that described in D was employed. However, because the distribution was random, the exact training set and testing set differed from those described in D. As shown in Table 9, the sample was set as having 15, 25, and 30 frames. For fairness, the same sequence of the training set and testing set was applied to the three conditions of frame numbers. The NN employed was model WRN_ex3d because its computational demand was lower than that of model WRN_ex3d_v2 and it was more accurate than the basic model and more stable than model WRN_ex3d_v3. Therefore, the results of model WRN_ex3d were more favorable.  
The results revealed that a low sampling frame number hindered accuracy, whereas a high sampling frame number evidently enhanced accuracy. This finding may have resulted from more feature data being contained in a single sample when the sampling frame number was higher. However, increasing the sampling frame number to enhance accuracy raised the computational demand and required more memory. This situation was particularly evident in the model that employed 3D convolution computation.
## 4 Functional Model of the Framework
### Level 3--Analysis of Historical Data
#### 4.3.1 First Step--Data Collection  
The first step is to collect data coming from the applications and devices used in the first and second levels of the model, which mostly measure the level of congestion at a specific time and place. We also rely on the accident database, which is mainly registered with government agencies.  
The data received from WSNs are initially collected within the cache in the fog node and then sent periodically to the cloud to be stored in the database permanently in the format shown in Figure 3. The congestion can be represented by any one of the following values--normal, moderate, heavy, stop. The data received from smart traffic lights are stored in a similar format. However, the data from mobile applications are stored in the format shown in Figure 3. The reason field can have any one of the following values--students leaving, employees returning, barrier, weather conditions, excavations and maintenance, accident. The data received from the LBS, such as Google Maps, are shown in Figure 3. The data received as a result of tweet analysis are stored in the format shown in Figure 3. Finally, we merge all the data into one database to reduce the volume of data by removing duplicates based on the location, time and date, and so on. Moreover, the database is also used for storing the data pertaining to accidents.
## 7 Results
### Case of Reference  
As we have explained at the beginning of this section, we have established a case of reference to compare the different solutions and the impact that the different parameters have on the reliability. We must note that, as we can see in Table 3, the number of replicas transmitted by PTRF in the case of reference is two, as this is the lowest number of replicas that PTRF can use while still providing time redundancy. Later on, we show how transmitting a single frame when using PTRF affects the reliability.  
Table 4 shows the results obtained in the case of reference for TSN, approach A and approach B. As we can see, the results obtained with all the solutions are in the same order of magnitude. However, TSN can achieve a reliability of 85.84%, while approaches A and B both reach a reliability of 99.99% when transmitting just two replicas.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**TSN** & **Approach A** & **Approach B** \\ \hline
0.858467899141365 & 0.999958504043796 & 0.99943599679439 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Reliability of TSN, approach A and approach B in the case of reference.  
If we now focus on PTRF, we can see that approach A is more reliable than approach B. This is because in approach A only the talker and the listener are PTRF-enabled devices and bridges are standard TSN devices with lower failure rate; unlike approach B where all the devices are PTRF-enabled. Since the BER in the case of reference is low, the contribution of the failure rate of the devices in the unreliability is higher than in networks with larger BERs and, thus, the unreliability of bridges is more noticeable.  
From this first set of results, we can extract several conclusions. First, we can conclude that the use of time redundancy has a positive impact on the reliability achievable in networks that suffer temporary faults. Second, we see that the benefits of time redundancy are even noticeable in networks with a relatively low BER, only \(1\times 10^{-10}\), and with a relatively small network, only six bridges. Finally, we can see that approach A is a more adequate solution than approach B in networks with low BER, as the contribution of the failure rate of the components increases the unreliability in the latter.  
In the following subsection we analyse how the BER affects reliability and we pay special attention to higher BER values.