esa-sceva/satcom-chunk-collection huggingface dataset:
# One-Class SVM Model-Based Tunnel Personnel Safety Detection Technology
## 4 Dataset Preparation
### Data Pre-Processing
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Data**} & \multirow{2}{*}{**Sensor Types**} & \multicolumn{2}{c}{**Statistical Characteristics**} & \multicolumn{1}{c}{**Early**} \\ \cline{3-6}  & & **Maximum** & **Minimum** & **Standard** & **Reference Value** & **Warning** \\  & & **Value** & **Value** & **Deviation** & & **Ratio** \\ \hline \multirow{4}{*}{Dataset 1} & Body Temperature Sensors & 37.0 & 34.9 & 0.66 & 36–37.2 \({}^{\circ}\)C & \\  & Heart Rate Sensor & 140 & 50 & 15.08 & 60–100 times/min & \\  & Ambient Temperature Sensor & 16.2 & 4.8 & 2.5859 & 5–20 \({}^{\circ}\)C & 0.1 \\  & Ambient Humidity Sensors & 83 & 45 & 11.0312 & 45–65\% & \\  & Relative Position Sensors & 8 & 1 & 1.7023 & & \\ \hline \multirow{4}{*}{Dataset 2} & Body Temperature Sensors & 37.2 & 35.1 & 0.71 & 36–37.2 \({}^{\circ}\)C & \\  & Heart Rate Sensor & 146 & 50 & 15.46 & 60–100 times/min & \\  & Ambient Temperature Sensor & 21.5 & 8.2 & 2.3316 & 5–20 \({}^{\circ}\)C & \\  & Ambient Humidity Sensors & 78 & 50 & 8.0320 & 45–65\% & \\  & Relative Position Sensors & 14 & 1 & 3.2348 & & \\ \hline \multirow{4}{*}{Dataset 3} & Body Temperature Sensors & 37.1 & 35 & 0.65 & 36-37.2 \({}^{\circ}\)C & \\  & Heart Rate Sensor & 144 & 50 & 14.25 & 60–100 times/min & \\ \cline{1-1}  & Ambient Temperature Sensor & 20.6 & 7.3 & 2.6042 & 5–20 \({}^{\circ}\)C & 0.08 \\ \cline{1-1}  & Ambient Humidity Sensors & 76 & 48 & 11.4427 & 45–65\% & \\ \cline{1-1}  & Relative Position Sensors & 11 & 1 & 2.5089 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Dataset characterization. Figure 7: (a) Before data enhancement; (b) After data enhancement. 2. For heart rate data, in this paper, 6%-10% of each dataset is selected to mark the outliers, and the processing method is to add increments conforming to a normal distribution (\(-\)10-30) to the actual data. 3. For the ambient temperature and humidity in the cave, 7%-10% of the dataset is selected for outlier labeling. 4. The relative position information contains a certain meaning for early warning criteria, so there is no need to construct anomalous data. All data are normalized as a means of converting physical values with wave function characteristics into some relative values that interact with other wave function characteristics by using dimensionless signal processing techniques to reduce the fallout from these quantities [8]. The normalization calculation method chosen in this paper is the method of maximum normalization (min-max normalization, mmn), which can also be referred to as maximum linear normalization or deviation normalization, which is a linear transformation of the original data so that the resulting value can be mapped between \([0,1]\) or some self-defined interval. The expression of the transformation function is as follows:  
\[X^{\prime}=\frac{X-min}{max-min}\]  
where \(max\) and \(min\) are the maximum and minimum values of the changed feature dataset, respectively. Some of the data after normalization of the data are shown in Table 7.
# Improving Energy Efficiency in LoRaWAN Networks with Multiple Gateways
###### Abstract  
LoRaWAN has imposed itself as a promising and suitable technology for massive machine-type communications. With the acceleration of deployment, improving the energy efficiency of LoRaWAN networks has become paramount, especially with the limitations of throughput and battery resources. However, LoRaWAN suffers from the Aloha access scheme, which leads to a high probability of collision at large scales, especially in dense environments such as cities. In this paper, we propose EE-LoRa, an algorithm to improve the energy efficiency of LoRaWAN networks with multiple gateways via spreading factor selection and power control. We proceed in two steps, where we first optimize the energy efficiency of the network, defined as the ratio between the throughput and consumed energy. Solving this problem involves determining the optimal node distribution among different spreading factors. Then, in the second step, power control is applied to minimize the transmission power at nodes without jeopardizing the reliability of communications. The simulation results show that our proposed algorithm greatly improves the energy efficiency of LoRaWAN networks compared to legacy LoRaWAN and relevant state-of-the-art algorithms.  
Internet of Things; LPWAN; LoRaWAN; energy efficiency; spreading factor; power control +
Footnote †: journal: _Sensors_
# Medium Access Control Protocols for Cognitive Radio Ad Hoc Networks: A Survey
## 3 Survey of State-Of-The-Art MAC Protocols
### Hybrid Protocols
#### 3.3.2 Partially-Observable Markov Decision Process-Based MAC  
The work in [80] proposes a protocol based on Partially-Observable Markov Decision Processes (POMDP). The protocol is partly a slotted single-radio MAC protocol. It divides time into slots where the CR user concludes the sharing of information about both the data and sensing. In each slot, data transmission is performed by using CSMA/CA with RTS-CTS.  
In this protocol, the partial Markov decision process is used since the network state cannot be completely seen because of sensing errors. To maximize the throughput, based on the past accumulated history of the spectrum band, the best set for long-term use is chosen for data transmission and a set for sensing. Upon successful data transmission, the decision of the channel is compensated.  
The authors in [80] assumed the transmitter and receiver switch their spectrum in a synchronized manner. Thus, it does not require additional CCC for this purpose. The protocol has a main drawback by assuming that the transmissions of PUs do not change over long periods of time. Moreover, since the protocol relies on the learning about the channel selection, in the initial stage, it shows a low performance, which can later be improved by time.
# Factors That Affect Liquefaction-Induced Lateral Spreading in Large Subduction Earthquakes
## 3 Validation of the Numerical Methodology
### Validation with Centrifuge Tests  
To evaluate the accuracy of the selected numerical methodology, several centrifuge tests were simulated. Table 2 gives a list of the centrifuge experiments that were used, where i = surface inclination, Dr = relative density of the liquefiable layer, H = thickness of the liquefiable layer, a\({}_{\text{max}}\) = maximum horizontal acceleration of the input ground motion, and Dh = residual lateral displacement at the surface. Table 3 shows a list of input parameters of the constitutive model, a suggested range of values recommended in Cyclic1D user's manual ([PERSON] et al. (2015) [26]) for saturated granular soil, and the calibrated model parameters for Nevada and Ottawa sand used in this study.  
The numerical methodology was previously validated against centrifuge experiments by other researchers ([PERSON] et al. (2002) [9]). In this study, we reproduced the experimental results from the projects VELACS ([PERSON] and [PERSON] (1993) [32], [PERSON] and [PERSON] (1998) [27]) and LEAP ([PERSON] et al. (2018) [33], [PERSON] (2018) [29]), in addition to the centrifuge tests from [PERSON] et al. (2003) [28].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Test** & **i [\({}^{\circ}\)]** & **Dr [\%]** & **H [m]** & **a\({}_{\text{max}}\) [g]** & **Dh [cm]** & **Reference** \\ \hline M1-2 & 0 & 40–45 & 10 & 0.23 & 1.7 & [PERSON] and [PERSON] (1998) [27] \\ M2-2 & 1.94 & 40–45 & 10 & 0.23 & 47.0 & [PERSON] and [PERSON] (1998) [27] \\ M2c-6 & 3.95 & 40–45 & 10 & 0.17 & 72.5 & [PERSON] and [PERSON] (1998) [27] \\ L45V2-10 & 2 & 45 & 10 & 0.23 & 66 & [PERSON] et al. (2003) [28] \\ L65V2-10 & 2 & 65 & 10 & 0.20 & 28 & [PERSON] et al. (2003) [28] \\ L75V2-10 & 2 & 75 & 10 & 0.21 & 23 & [PERSON] et al. (2003) [28] \\ RPI-02 & 5 & 65 & 5 & 0.17 & 35 & [PERSON] (2018) [29] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Centrifuge tests used to validate the numerical methodology.
## 3 Results and Discussion
### Effect of LED Light Treatments on Nutrients
The total dietary fiber content of fresh red cabbage and steamed green cabbage was 23.1 g 100 g\({}^{-1}\) DW, with IDF as the predominant fraction [33]. In addition, the authors showed that the IDF fraction was also dominated by neutral sugars, but contrary to the results for microgreens, the content of uronic acids exceeded the amount of Klason lignin. Furthermore, contrary to our results, in mature forms of cabbage, uronic acids dominated over neutral sugars in the SDF. The presented differences may indicate the transformation of fiber components during the further growth of cabbage. To the best of our knowledge, this is probably the first study looking into the dietary fiber profiles of white and red cabbage microgreens. \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Factor**} & \multicolumn{3}{c}{**White Cabbage Microgreens**} & \multicolumn{3}{c}{**Red Cabbage Microgreens**} \\ \cline{2-7}  & **White** & **White + Red** & **White + Blue** & **White & **White + Red** & **White + Blue** \\ \hline Ash & 21.36 \(\pm\) 0.21 c & 18.79 \(\pm\) 0.47 b & 16.06 \(\pm\) 0.23 a & 25.60 \(\pm\) 0.37 c & 20.14 \(\pm\) 0.03 b & 19.15 \(\pm\) 0.12 a \\ Protein & 30.48 \(\pm\) 1.29 c & 13.66 \(\pm\) 0.71 b & 10.67 \(\pm\) 0.07 a & 29.27 \(\pm\) 0.95 b & 16.70 \(\pm\) 0.14 a & 15.94 \(\pm\) 0.65 a \\ Fat & 7.33 \(\pm\) 0.27 c & 5.05 \(\pm\) 0.14 b & 3.78 \(\pm\) 0.11 a & 7.97 \(\pm\) 0.30 b & 5.36 \(\pm\) 3.00 a & 4.90 \(\pm\) 0.21 a \\ Sugars & 2.43 \(\pm\) 0.12 a & 6.05 \(\pm\) 0.38 b & 9.76 \(\pm\) 0.13 c & 5.84 \(\pm\) 0.26 a & 6.57 \(\pm\) 0.47 ab & 6.90 \(\pm\) 0.23 b \\ Fiber total & 39.07 \(\pm\) 0.30 a & 55.42 \(\pm\) 1.16 b & 53.69 \(\pm\) 2.03 b & 35.49 \(\pm\) 0.30 a & 41.18 \(\pm\) 1.08 b & 38.21 \(\pm\) 0.55 a \\ SDF total & 1.53 \(\pm\) 0.10 a & 2.71 \(\pm\) 0.02 b & 3.51 \(\pm\) 0.22 c & 2.12 \(\pm\) 0.10 b & 1.90 \(\pm\) 0.10 ab & 1.67 \(\pm\) 0.12 a \\ IDF total & 37.54 \(\pm\) 0.41 a & 52.71 \(\pm\) 1.18 b & 50.18 \(\pm\) 1.82 b & 33.37 \(\pm\) 0.39 a & 39.28 \(\pm\) 0.89 b & 36.54 \(\pm\) 0.43 a \\ \hline \end{tabular} Values are expressed as mean \(\pm\) SD (\(n\) = 3); values with different letters in each row represent significant differences at \(p<0.05\) separately for each cultivar. SDF—soluble dietary fiber; IDF—soluble dietary fiber.  
\end{table} 
Table 2: Effect of LED light treatments (white, white with red, and white with blue) on the elementary chemical composition (g 100 g\({}^{-1}\) DW) of white and red cabbage microgreens.
## 3 Results and Discussion
### Test Results of the Fiber-Reinforced LPC
#### 3.2.2 Flexural Strength and Splitting Tensile Strength of the LPC  
Flexural strength and splitting tensile strength are commonly used to estimate the tensile strength of concrete, although their results will not be the same. This is mainly due to the different failure mechanisms of the two. Flexural strength is determined by the bending stress at the failure section. However, splitting tensile strength is determined by the pure tensile stress generated along the diameter of the specimen at the point of failure caused by the compressive load. Therefore, split tensile strength is more sensitive to microcracks in the concrete. Table 11 shows that the flexural strength of LPC at 14 days of age was between 0.84 and 1.55 MPa. As is clear from Figure 8a, the flexural strength of the LC7 mix was the lowest (0.84 MPa) and the flexural strength of the LC2 mix was the highest (1.55 MPa). As the age of the specimen increased from 14 days to 28 days,  
Figure 7: The test results of LPC: (**a**) compressive strength and (**b**) elastic modulus.  
the flexural strength also increased. The flexural strength at 28 days of age was between 1.19 and 1.86 MPa, as shown in Table 11. In contrast, the 28-day flexural strength of LPC prepared by other researchers ranged from 0.5 to 1.4 MPa, as shown in Table 2[46; 47]. This result clearly shows that the 28-day flexural strength of the LPC prepared in this study was relatively good. The reason should be the polypropylene fiber used in this study. In addition, the flexural strength of the LC5 mix was the lowest (1.19 MPa) and the flexural strength of the LC6 mix was the highest (1.86 MPa), as shown in Figure 8a.  
As for the splitting tensile strength, it can be seen from Table 11 that the splitting tensile strength of the concrete at 14 days of age was between 0.70 and 1.11 MPa. It is clearly seen from Figure 8b that the LC4 mix had the lowest splitting tensile strength (0.70 MPa) and the LC6 mix had the highest splitting tensile strength (0.99 MPa). Similarly, as the age of each specimen increased from 14 days to 28 days, the splitting tensile strength also increased. At 28 days of age, the splitting tensile strength was between 0.78 and 1.11 MPa, and its average value was about 64% of the average flexural strength. Similarly, compared with other researchers, the 28-day splitting tensile strength of the LPC prepared with different lightweight aggregates ranged from 0.5 to 1.4 MPa, as shown in Table 2[46; 47]. In view of this, the 28-day splitting tensile strength of the LPC prepared in this study was still relatively good due to the use of polypropylene fiber. The ratio of the 28-day splitting tensile strength to the 28-day compressive strength ranged from 11% to 22%, with an average of 16.5%. They were higher than those of the PC containing natural aggregate, which ranged from 9-14%[44]. Furthermore, the LC2 mix had the lowest splitting tensile strength (0.78 MPa) and the LC9 mix had the highest splitting tensile strength (1.11 MPa), as shown in Figure 8b.
# An Analysis of Methods and Metrics for Task Scheduling in Fog Computing
## 5 Taxonomy for Task Scheduling Algorithms in Fog Computing
### Optimization Criteria
#### 5.1.4 Response, Execution, and Completion Time  
Response time is the time between sending a request to the server and completing the task execution. An increase in the response time will cause latency and delays. Response time includes both the network latency and the time for the computation. The response time of processing task \(t_{i}\) on fog node \(f_{j}\) is calculated as the sum of the specified node's execution time plus the task's transmission time from the source to the destination [46]:  
\[RT(X_{ij})=Et(X_{ij})+T_{r}t_{total}(X_{ij})\]  
where \(X_{ij}\) refers to the assignment of task \(t_{i}\) to fog node \(f_{j}\), \(T_{r}t_{total}(X_{ij})\) is the total transmission time of tasks from the edge device to the appropriate fog node \(f_{j}\), and \(Et(X_{ij})\) is the execution time of the task in the fog node.  
If the task is processed locally, the response time equals the local serving time. If a task is scheduled, the total response time is the sum of the task transmission time to the fog node through the wireless network, the processing time on the fog node, and the transmission time back to the IoT devices. If the fog node is overloaded, the processing time at the fog is replaced by the transmission time between the fog node and the remote cloud, the processing time on the cloud and the transmission time back to the fog node [65].  
Execution time is the total run time for the fog application, and it is measured in milliseconds. For task computing on a fog server, the processing of task n is divided into two phases. The first phase is the transmitting phase where the industrial sensor sends task data to fog through wireless transmission. The second phase is the fog-computing phase where task n is executed in the fog. Fog processing delay for each task is the sum of the delay due to transmitting task data through wireless links and fog-server computing time. The formula is as follows [4]:  
\[T_{n,m}^{f}=\frac{d_{n,m}}{r_{n,m}}+\frac{C_{n,m}}{f_{n,m}^{f}}\]  
where \(f_{n,m}^{f}\) the computational demand of a fog server for a particular task (n) as perceived from the perspective of a specific fog node (m). Here, n indicates task number, and m indicates the fog node number. \(C_{n,m}\) is the completion time, \(d_{n,m}\) is the input data size, and \(r_{n,m}\) is the data transmission rate for task \(n\).  
Completion time of a task \(n\) is determined as the time it takes to complete execution. It includes the wait and processing times of the task. The completion time \(CT_{n,m}\) of a task n can be expressed as follows [4]:  
\[CT_{n,m}=RT_{n,m}+T_{n,m}\]  
where \(T_{n,m}\) defines the execution time of the task n and \(RT_{n,m}\) represents ready time that the earliest completion time for all its dependent tasks.
## 4 Review: Slant Links Rain Fade Models
### Statistical Models  
#### 4.2.1 Maseng-Bakken (M-K) Model  
This model [90] is based on the log-normal distribution of rain and utilizes a 1-dimensional Gaussian Markov process (generated by a memoryless non-linear device for transforming attenuation and rain intensity, Figure 4). With the assumption that the attenuation rate is constant throughout the path, rainfall intensity is given by Equation (30):  
\[\gamma=kR^{\alpha}L \tag{30}\]  
The symbols \(k\) and \(\alpha\) denote radio wave propagation parameters that depend on the frequency and polarization, and \(L\) is the length of the link. It was examined that the \(\gamma\) is log-normal distributed. Consequently, following the log-normal distribution, rainfall intensity can be written as: \(\Lambda(R_{m},\,\sigma_{R}{}^{2};R)\). The justification of the modeling of the rainfall intensity distribution is given in Equation (31):  
\[\Lambda((\gamma_{m}/k\cdot L)^{1/\alpha}m\sigma_{\gamma}^{2}/\alpha^{2};R)= \Lambda(R_{m},\,\sigma_{R}{}^{2};\,R) \tag{31}\]The conformity of mapping the distribution of rainfall intensity through \(\gamma\) can be accessed through determining the auto-correlation between the variables, as in Equation (32):  
\[R_{\gamma}(\tau)=\gamma_{m}^{2}\exp\Bigl{(}\sigma_{\alpha}^{2}(1+\rho(\tau)) \Bigr{)} \tag{32}\]  
After normalization and the removing the bias:  
\[k_{\gamma}(\tau)=\frac{\exp\Bigl{(}\sigma_{\gamma}^{2}\rho(\tau)\Bigr{)}-1}{ \exp\Bigl{(}\sigma_{\gamma}^{2}\Bigr{)}-1} \tag{33}\]  
where \(\rho(\tau)=\exp(-\beta|\tau|)\) and the value of \(\beta\) can be determined as:  
\[\beta=E\Bigl{\{}(\gamma(t+\Delta t)-\gamma(t))^{2}/\gamma(t)\Bigr{\}}/2\sigma_ {\alpha}^{2}\Delta t \tag{34}\]  
where \(\Delta t\) is the sampling time interval determined from the time series of \(\gamma\). A constraint of the M-K model is that the supporting frequency band only goes up to 35 GHz. This model has not been validated with heavy rainfall or with rainfall data from various locations around the world. A modified version of the M-K model, which introduced an offset parameter called the \"calibration factor\" after the memoryless non-linear device, was reported in [110]. The sequential steps of the model are given in Algorithm 10.  
Advantages  
The model is straightforward, which allows simulation of communication link functionalities.  
Limitations  
The model has not been verified with diverse weather conditions such as low to high rainfall or rain type.
## 4 Simulation Results
### The Localization Error of ACFL and PCFL under Different Parameters
#### 4.1.3 The Density of Anchors  
In Figure 7 the threshold is set to be 0.0142 and the number of samples is set to be 400. \(N_{\mathit{anchor}}\) stands for the average number of task anchors in the communication radius \(R\) and is assumed to change from 5 to 100. We assume that \(V\) denotes the volume of the simulation space. \(D_{\mathit{anchor}}\) is defined as the density of task anchors and it can be calculated by the formula \(D_{\mathit{anchor}}=(\dfrac{4}{3}\pi R^{3})N_{\mathit{anchor}}/V\)[17], so we can obtain different values of the localization error when \(D_{\mathit{anchor}}\) varies from 0.5 to 5 in the simulation experiment. We can filter out more task anchors with the increase of \(D_{\mathit{anchor}}\). The localization error of PCFL (or ACFL) is inversely proportional to \(D_{\mathit{anchor}}\) and decreases obviously along with the increase of \(D_{\mathit{anchor}}\) as shown in Figure 7. Because PCFL (or ACFL) can convert three-dimensional localization algorithm to two-dimensional scenario, it can give accurate location and good reliability.
## 2 Materials and Methods
### Effective Moisture Diffusivity  
To measure the \(D_{\textit{eff}}\), the diagram of the drying kinetics and the Crank equation based on the second Fick's law were applied. To this end, the \(MC\) was plotted versus the time of the drying process. Then, the moisture ratio was determined, and its logarithmic diagram was drawn versus time and fitted with a straight line. The second Fick's law deals with the mass diffusivity during the drying process's descending phase, and it may be solved for a variety of geometries by employing the right boundary conditions. Equation (2) can be used to calculate the Fick equation for a thin layer:  
\[MR=\frac{M_{t}-M_{e}}{M_{b}-M_{e}}=\frac{8}{\pi^{2}}\sum_{n=1}^{\infty}\frac{ 1}{(2n+1)}exp\left(\frac{-D_{\textit{eff}}(2n+1)^{2}\pi^{2}t}{4L^{2}}\right) \tag{2}\]  
In which \(L\) denotes the sample thickness (m); \(n\) shows the number of the drying terms (1, 2, 3, 4, \(\ldots\)). \(M_{t}\), \(M_{e}\), and \(M_{b}\) represent the sample moisture at time of \(t\), equilibrium, and initial \(MC\) (d.b.), respectively. \(MR\) shows the moisture ratio, while t stands for the \(D_{t}\). \(D_{\textit{eff}}\) is also expressed in m\({}^{2}\)/s.  
If both sides of the equation are considered a natural logarithm, Equation (3) is obtained which is a straight line [26]. Putting the slope of this line equal with the slope of the fitted line of the natural logarithm, the value of \(D_{\textit{eff}}\) can be determined (Equation (4)) [27].  
\[ln(MR)=ln(\frac{8}{\pi^{2}})-ln\left(\frac{-D_{\textit{eff}}\pi^{2}t}{4L^{2}}\right) \tag{3}\]  
\[Slope=-D_{\textit{eff}}\frac{\pi^{2}}{4L^{2}} \tag{4}\]