esa-sceva/satcom-chunk-collection huggingface dataset:
# IoT-Enabled Gas Sensors: Technologies, Applications, and Opportunities
## 6 IoT-Based Wireless Gas Sensors  
Over the last 20 years, the research and development of smart sensors for different purposes have become essential in many areas, such as environmental monitoring and pollution control, residential and industrial automation, protective equipment and assisted living devices [3, 6, 7, 142]. The necessity of remote monitoring of numerous parameters has led to the development of wireless sensors in numerous fields. Moreover, wireless sensors and the construction of WSANs facilitated the automation process, data collection, transfer, processing, and storage. More recently, these smart sensors have acquired the capacity of performing machine-to-machine (M2M) communications, simplifying the communication and interoperation between devices, granting numerous possibilities in control systems and automation [11, 12].  
Many authors have focused on the proposal and creation of gas sensors to attend to specific target gases in particular conditions [32, 34, 103, 108, 110]. As examples, [PERSON] et al. [32] proposed an oxygen gas sensor for personal protective equipment without wireless transmissions; in [34] the authors have developed a CO\({}_{2}\) gas sensor for remotely monitoring the levels of this gas, transferring the data through the General Packet Radio System (GPRS).  
Recently, systems for indoor air quality monitoring and even to control gas leakages based on the data collected by smart sensors have been proposed [93, 103, 104, 108]. They present different characteristics whether they are to do with the sensing mechanism or the implemented communication protocols. Many contributions towards wireless gas sensors and IoT-enabled gas sensors are only published on proceedings, and to the best of authors knowledge, almost no commercial solution is available on the market. In this section, the main characteristics and requirements of IoT-enabled gas sensors and the most relevant smart gas sensors proposed in the literature will be reviewed, emphasizing the main characteristics of the proposals.
## 5 Results and Data Analysis
### RQ1: Maintainability Measurement in a Software System
The \(d\) effect size ranges from huge (for \(Ml_{impr.}-Ml_{orig. }\), \(Ml_{LOC}-Ml_{orig. }\), \(Ml_{impr.(CM)}-Ml_{orig. (CM)}\), \(Ml_{LOC}-Ml_{JH}\)), very large (for \(Ml_{LOC}-Ml_{impr. }\)), to very small (for \(Ml_{JH}-Ml_{orig. }\), \(Ml_{JH(CM)}-Ml_{orig.(CM)}\)). For the last one, this means that the practical significance of the difference is very small. For all cases where the \(r_{c}\) effect size was used, its value equals \(-1\) or \(1\). Where a positive effect size of \(1\) is reported, the Index variant values in the reference group are, in all cases, higher than the Index variant values in the comparison group (e.g., in the case of \(Ml_{orig.(CM)}-Ml_{orig.}\)). Contrariwise, when a negative effect size of \(-1\) is reported, the Index variant values in the comparison group are, in all cases, higher than the Index variant values in the reference group (e.g., in the case of \(Ml_{orig.}-Ml_{impr.(CM)}\)). Figure 4: Boxplots of the Index variants values with statistical significance annotation (n.s. = not significant).
## 3 Results and Discussion  
### Validation of the Plain Tube  
Nusselt number correlation from Petukhov  
\[Nu=\frac{(f/8)RePr}{1.07+12.7\left(f/8\right)^{1/2}\left(Pr^{2/3}-1\right)}, \tag{14}\]  
for \(10^{4}<Re<5\times 10^{6}\).  
Nusselt number correlation from Gnielinski  
\[Nu=\frac{\left(f/8\right)\left(Re-1000\right)Pr}{1+12.7\left(f/8\right)^{1/2} \left(Pr^{2/3}-1\right)}, \tag{15}\]  
for \(10^{3}<Re<5\times 10^{6}\).  
Friction factor correlation from Blasius  
\[f=0.3164 Re^{-0.25}, \tag{16}\]  
for \(4\times 10^{3}<Re<10^{5}\)  
Figures 4 and 5 compare the experimental data and the data obtained from the standard correlation estimation.  
It is evident that the Nusselt number agrees well with the predicted results of the empirical correlations of [PERSON] and Gnielinski with less than \(\pm\)2.8% and \(\pm\)7.1% deviation, respectively, and friction factor values agree with Blasius correlation with less than \(\pm\)2.5% deviation.
## 7 Security Analysis
### Attack Analysis
#### 7.2.2 Replay Attack  
In a replay attack, an internal or outsider attacker gathers a legitimate message of the system and replays it later to negatively affect the system. In Hyperledger Fabric, all transactions are protected from replay attacks by using a unique nonce [53]. This means that each transaction can be uploaded to the blockchain only once.
## 3 Related Work
The capacity and load of each VM are initially computed. If the load is larger than the threshold value, then the load balancing algorithm allocates tasks to the VMs. The deciding factor of VMs is calculated, and the loads of VMs are checked by the algorithm. The selection factor for each task is then calculated, and the task with a better selection factor than those of others is allocated to the VMs. Reference [24] proposed the raven roosting optimization algorithm that uses an efficient load balancing method to solve the task scheduling problem, where roosts represent information centers. VM capacity is determined to transfer overloaded tasks. References [25; 26] introduced the deadline-aware priority scheduling model, which minimizes the average makespan and maximizes resource utilization under deadline constraint. Tasks are scheduled in ascending order based on length priority and matched to the suitable VM with minimum processing time. Reference [27] proposed the uncertainty-aware online scheduling algorithm for enhancing the performance of cloud service platforms. The count of tasks that directly wait on VMs is controlled, and proactive and reactive strategies are combined. The feature parameters of applications are computed after they are submitted, and tasks are allocated to service instances for execution. Reference [28] improved the energy efficiency of heterogeneous servers in a cloud computing system via a noncooperative game-based model. The model represents server utility function as unit power efficiency and proves the existence of the Nash equilibrium point of game. The total request amount in the system serves as a constraint, and the appropriate task quantity is selected by each server that matches it with the optimum resource to handle user request. The Nash equilibrium is solved by a proposed Lagrange multiplier-based distributive iterative algorithm, but the shadow values of this algorithm lead to the low accuracy of the model. Reference [29] provided a deadline-aware PSO algorithm for optimizing the performance of task scheduling algorithms. Two parameters, namely deadline and profit, are utilized by the algorithm and calculated to classify tasks. Each task is scheduled to VMs on the basis of priority using the PSO algorithm. If two tasks have the same deadline values, then the maximum user pay value, which considers deadline and million instructions, is computed. Tasks are scheduled on the basis of value. However, the PSO algorithm exhibits low convergence time.
# Stability Criterion for Dynamic Gaits of Quadruped Robot
## 5 Results and Discussion
### Simulation 3  
Simulation 3 was designed to prove that the stability-evaluation method proposed in this paper is also applicable to the motion of a robot in 3D space (Figure 29). The robot moved on a 12\({}^{\circ}\) slope as shown in the animation (Video S3), and the body's trajectory was similar to that used in Test 3 of the previous simulation: CoG started to move from the midpoint of the current support line to that of the next support line at a constant speed when the previous support line had just disappeared. The contact parameters between each foot and the ground and the trot-gait parameters of this simulation were set to be identical to Test 3.  
The attitude angles of the principal-axes coordinate system, and the variation of the stability measurements calculated using above three methods with time are shown in Figure 30a,b.  
Figure 30: (**a**) Attitude angles; and (**b**) stability measurements of Simulation 3.  
Figure 28: Stability measurements using ZMP and ZMP\({}_{0}\).  
Figure 29: Simulation of Simulation 3.  
The movement of the robot trotting on the slope was steady. The average values (over 15 s) of the stability evaluations obtained using these three methods of Simulation 3 and Test 3 are listed in Table 7.  
Method 1 showed that those two motions had almost the same stability. Method 2 indicated that the stability of the motion on slope was much higher than that on horizontal plane, which was contrary to the result of Method 3. Three same impact forces (as shown in Table 6) were applied to the CoM of the body during Simulation 3. The posture angles of the principal-axes coordinate system, the positions and velocities of the body are shown in Figure 31a-c, respectively.  
The robot did not lose its balance before the last impact, but its feet skidded on the slope after the first impact because it was hard for the ground to provide enough friction to push the robot upward. The wave heights of the posture angles were not able to recover to the states before impact. Therefore, the robot was more likely to lose its balance on the slope, and the stability of this motion was worse than in Test 3. Three Methods were used to analyze the motion on slope with impacts (Figure 32). The stability-evaluation method proposed in this paper could measure the stability of motion in 3D space more accurately and efficiently than Methods 1 and 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Method 1** & **Method 2** & **Method 3** \\ \hline On slope (Simulation 3) & 0.0278 & 0.9007 & 0.1354 \\ On horizontal plane (Test 3) & 0.0277 & 0.8630 & 0.1402 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Average values of stability evaluations when the robot trotting on the 12\({}^{\circ}\) slope and horizontal plane.  
Figure 31: Sensor data of the motion with impact in Simulation 3: (**a**) posture angles of the principal-axes coordinate system; (**b**) positions; and (**c**) velocities of the body.
## 3 Results
It has to be mentioned that corrosion of the glazed porcelain crucible and additional phases in SEM microphotographs were not observed. The presence of aluminum or silicon as the main elements of the crucible was not detected in EDX analysis. [PERSON] et al. [33] also showed that there is no interaction between the sodium nitrate melt and the porcelain glazed crucible. The results of thermal analysis are shown in Figures 3 and 4. DSC analysis of Sample 3 in argon atmosphere was made up to 1300 \({}^{\circ}\)C (Figure 3). Peaks with maximums at 986 \({}^{\circ}\)C and 1010 \({}^{\circ}\)C correspond to phase reactions, and the effect with a maximum at 1074 \({}^{\circ}\)C corresponds to the end of melting of the sample (liquidus point). DSC data show that Sample 3 is stable in the flow of argon until 962.5 \({}^{\circ}\)C.  
Thermal stability in air also was investigated (Figure 4). Sample 3 starts to lose weight at approximately 290 \({}^{\circ}\)C. The endothermal peak around 80 \({}^{\circ}\)C may be attributed to dehydration of adsorbed water. There are two exothermal peaks that could be attributed to the chemical reaction of pyrohydrolysis [34]. However, the X-ray pattern of Sample 3 did not change after this DSC experiment. \begin{table}
\begin{tabular}{c c c c c} \hline
**Sample \(\mathbf{\lambda}\)** & _a,_ \(\mathbf{\dot{\mathrm{A}}}\) & _c,_ \(\mathbf{\dot{\mathrm{A}}}\) & \(\mathbf{\mathrm{V}_{r}\mathrm{\AA^{3}}}\) & \(\mathbf{\mathrm{D}_{\mathbf{\omega}}\mathrm{\ g/cm^{3}}}\) \\ \hline
1 & 4.164 (1) & 5.946 (1) & 103.09 (2) & 6.039 \\ \hline
2 & 4.122 (1) & 17.672 (1) & 300.30 (3) & 6.310 \\ \hline
3 & 4.122 (1) & 17.674 (1) & 300.37 (1) & 6.150 \\ \hline
4 & 4.124 (1) & 17.682 (1) & 300.69 (1) & 6.167 \\ \hline
5 & 4.123 (1) & 17.683 (1) & 300.61 (1) & 6.242 \\ \hline \end{tabular}
\end{table} 
Table 2: Crystal lattice parameters and sample density. Figure 3: DSC curve of Sample 3 in argon at heating rate 20 K\(\times\)min\({}^{-1}\). Figure 5 shows the luminescence spectra in the visible range for Sample 5 at different temperatures (Figure 5a) and in the SWIR range for Samples 3 and 5 at a temperature of 296 K (Figure 5b). The peak in the SWIR region at 1150 nm wavelength corresponds to Ho\({}^{3+}\) (\({}^{5}\)I\({}_{6}\)\(\rightarrow\)\({}^{5}\)I\({}_{8}\)), and the peak at 1550 nm corresponds to Er\({}^{3+}\) (\({}^{4}\)I\({}_{13/2}\)\(\rightarrow\)\({}^{4}\)I\({}_{15/2}\)). The temperature dependences of the ratio of the intensities of the red and green luminescence bands for the visible range of the spectrum are shown in Figure 6a, and the intensity ratios of the erbium and holmium luminescence bands for the SWIR range are shown in Figure 6b. From the dependences presented in Figure 6, the relative coefficients of temperature sensitivity in the temperature range from 24 to 44 \({}^{\circ}\)C are determined: S\({}_{\rm r}\) = 5.55%\(\times\)K\({}^{-1}\) in the visible range and S\({}_{\rm r}\) = 0.34%\(\times\)K\({}^{-1}\) in the SWIR range. Figure 4: DSC curve of Sample 3 in air at heating rate 20 K\(\times\)min\({}^{-1}\).
# Towards a Cyber-Physical Range for the Integrated Navigation System (INS) System (INS)
## 2 Related Work
### Testbeds
In contrast, the HiCASS testbed was specifically developed for testing the HiCASS system. The eMIR platform is quite flexible in terms of capabilities, which include risk assessment and performance tests of the components. The data received from the sensors onboard vessels, or from real individual sensors, or mimic sensor data can be used by the eMIR. On the other hand, the HiCASS testbed is deployed on a real vessel and can run only with real sensor data, such as those from AIS, RAdio Detection And Ranging (RADAR), anemometer, GPS, and speed log. The eMIR includes tools for various simulation and analysis purposes, e.g., human behavior, realistic environment (i.e., wind, wave, current), marine traffic, and cargo behavior analyses. Moreover, an experimental VTS exists for the eMIR platform. On the other hand, given that direct sensor data onboard are used, Hyundai's testbed does not include any simulation tools. Many details and tools regarding the eMIR platform can be found in the open literature; in contrast, available details of the HiCASS testbed are limited. Even though mobility is a feature of both testbeds, a fixed platform of the eMIR is available as well. Both platforms were tested onboard ships. Moreover, a sensor box to connect sensors was developed in both. The eMIR's sensor box has the ability to transfer data to distant locations via LTE. Hyundai's testbed does not have such a function.
# Interval Feature Transformation for Time Series Classification Using Perceptually Important Points
## 3 Interval Feature Transformation
### Discriminative Feature Vector Selection
#### 3.2.2 Selection Process  
Interval feature vector selections are algorithms that attempt to identify and remove as much irrelevant and redundant information as possible prior to learning. In this paper, we try to extract \(k\) discriminative feature vectors. The corresponding algorithm contains three major steps:  
Firstly, the algorithm performs a single scan of all candidate feature vectors and removes the duplicate and similar vectors, which are vectors with the same mean and variance values, or vectors with the same skewness and kurtosis values. Then it can obtain a final candidate list \(\langle c_{1},\ c_{2},\ c_{3},\cdots c_{l}\rangle\).  
Secondly, the algorithm calculates the distance between each candidate \(c\) and each time series. In machine learning, cosine similarity is often adopted to express the similarity between two vectors. The algorithm measures the cosine of the angle between two vectors projected in a multi-dimensional space. Vectors that are very similar to each other have a cosine similarity that is close to 1. Vectors that are nearly orthogonal have a cosine similarity near 0. Vectors that point in opposite directions have a cosine similarity of \(-1\). Let x and y be two feature vectors for comparison, the cosine similarity measure between x and y is given by Equation (7):  
\[sim(x,y)=\frac{xy}{\|x\|\ \|y\|} \tag{7}\]  
The cosine distance can be calculated by Equation (8):  
\[cdis(x,y)=1-sim(x,y) \tag{8}\]  
In this paper, the distance between candidate \(c\) and each time series \(T_{i}\) is defined as the minimum value of the cosine distance between \(c\) and each \(f\) in the \(T_{i}\). If \(c=\langle\mu,\ \sigma,\ d,IQR,\) SKEW, \(KURT\rangle\) and \(T_{i}=\langle f_{11},\ f_{22},\ f_{33},\cdots f_{i\omega}\rangle\), then the cosine distance from \(c\) to \(T_{i}\) is defined as Equation (9).  
\[\mathrm{dist}(c,T_{i})=\min_{\mathrm{j}\in(0,\omega)}\left(\mathrm{dist}\left( c,f_{ij}\right)\right) \tag{9}\]  
After comparing the similarity of each candidate \(c\) and all the time series in \(T\), the calculation result of each candidate will be ordered to search the split point and get the information gain (\(IG\)) as described in Section 3.2.3.  
Finally, all the candidates are accessed, and they are sorted according to the \(IG_{c}\). Then, we select the \(k\) feature vectors with the biggest \(IG_{c}\) as the discriminative features.  
In general, discriminative features can be extracted by comparing the \(IG_{c}\) of every candidate.
# Application of Machine Learning in Electromagnetics: Mini-Review
## 2 Familiarization with ML  
During the past few decades, researchers have been aiming at making machines intelligent by providing them the capacity to make decisions or classify objects without being programmed or without the use of predefined functions, rather using raw data from the environment. Termed Artificial Intelligence (AI), the field has made breakthroughs in the past few years. Mimicking the brain to solve an unknown problem, AI is widely used in the fields of engineering, science, medicine, and technology [26]. ML is a branch of AI that can be used to train algorithms to learn from and act on data without being explicitly programmed [27], and the learned model can be used for future designs. Currently, ML is used in interpreting and analyzing data more efficiently and effectively than the human brain. Incorporating the statistical modeling-based prediction and optimization techniques, ML has various models and approaches as different problems need different approximations to provide optimized solutions [28]. Common ML algorithms include artificial neural networks (ANNs), support vector machines (SVMs), k-nearest neighbors (KNNs), random forest, and gradient boosting trees [29; 30; 31]. From a methodological standpoint, ML can be classified into three main types: Supervised, unsupervised, and reinforcement learning [32; 33; 34].
# A Microservice-Based Smart Agriculture System to Detect Animal Intrusion at the Edge
## 6 Evaluation
#### 5.1.3 Position Prediction  
We used a PC as a fog server in our experiments. It was placed 10 m above the ground to increase the transmission speed with end nodes [27]. The prediction algorithm ran continuously waiting for data. To evaluate our algorithm, we made use of our ground truth data, wherein the container extracted three sets of location data from a movement, used the first two sets of data to predict the location for the time corresponding to the third set of data, and then compared this predicted location with the actual location to assess the accuracy of the prediction. One measure of accuracy we used is the distance offset, which is the distance between the predicted location and the actual location. We measured distance offsets for all movements for which we have at least three location values. For movements such as M2 (Table 2), for which we have more than three location values, we measured distance offset for each triplet of location values resulting in 10 distance offsets measured. Figure 9 shows the average distance offset of each movement.  
As we can see, the average distance offset is relatively low (less than 5 m) for most movements and layouts. We observe that Layout B shows relatively small distance offsets in most of the movement tests, although in M1, it had a higher offset in prediction than the other two layouts. However, in M8, Layout B did not have sufficient readings for the algorithm to make predictions due to the presence of some blind triangles near the boundary where the sensor cannot detect the animal once it moves there. Layout C produced the largest distance offsets in most of the tests due to the presence of many blind areas inside the coverage area, which prevented the animal from being detected quickly and continuously, resulting in more inaccurate predictions. Layout A performed moderately and without data loss, which is due to its continuous and extensive coverage area. Based on our experiments and the analysis in Section 2, we recommend Layout B as the optimal sensor deployment method, which can be readily applied to rectangular fields of varying dimensions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Layout A** & **Layout B** & **Layout C** \\ \hline
1 & A—(20, 1.5) & A—(20, 3.5) & A—(20, 5.5) \\ \hline
2 & A—(15, 1.5) & A—(15, 3.5) & A—(15, 5.5) \\ \hline
3 & A—(15, 1.5) & A—15, 3.5) & A—(15, 5.5) \\ \hline
4 & A—(10, 1.5) & A—(10, 3.5) & A—(10, 5.5) \\ \hline
5 & A—(5, 1.5) & A—(5, 3.5) & A—(5, 5.5) \\ \hline \hline \end{tabular}
\end{table}
Table 2: M2 location values: side—coordinates (x, y).  
Figure 8: Movement trajectories.
## 5 Potential Implementation
### Degradation Analysis  
A wind turbine is a complex electromechanical system, and its performance changes in accordance with the degradation trend of a repairable system. Taking the HV curve of T07 in Figure 14 as an example, it is easy to note that the HV curve trend is similar to the theoretical change trend of a repairable system if the maximum and minimum points are connected. In other words, although the performance of the equipment degrades after operation, it can be improved after repair. Subsequently, the operational phase of the equipment can be determined, and an efficient maintenance plan can be formulated. If we connect the peak points in Figure 12, a notable change trend of the health value before and after the repair can be found. More importantly, the degradation level of the equipment can be quantified through the HV. In Figure 12, the health value of Point E is large, which indicates poor stationarity of the wind turbine. After a series of maintenance activities is carried out, the HV is continuous and stably decreases, eventually reaching the ideal Point F eventually. It can be concluded that the PAM method can act as a valuable tool for analyzing the performance degradation of wind turbines.