esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Contour Profile Inspection
#### 4.1.2 Form Deviation Analysis on Line Profile  
The line profile analysis presented in Figure 28 outlines the trends in form deviation, showing the correlation with \(M_{\Delta}(k)\) values obtained from the \(I_{d,L}(x,y)^{SG}\). The materials involved in this analysis include aluminum, nylon, delrin, and steel.  
Starting with aluminum, which has \(M_{\Delta,L}(k)=0.3614\), the analysis reveals moderate form deviations in its line profile. The results show that aluminum maintains relatively good accuracy in its line profile, but there are discernible deviations from the ideal form. This information is crucial for applications where precision in the line profile is a critical factor.  
Nylon, with \(M_{\Delta,L}(k)=0.2518\), exhibits lower form deviations than aluminum. The results show that nylon's line profile is closer to the ideal form, indicating higher accuracy and potentially making it suitable for applications where stringent form requirements are essential.  
Delrin stands out with a significantly higher \(M_{\Delta,L}(k)=1.078\), signaling a more substantial level of form deviations in its line profile. This information is essential for applications where precision and conformity to the ideal form are critical, as the deviations in delrin may impact its suitability for certain use cases.  
Figure 27: Form deviation: curve probability plot.  
Steel, with \(M_{\Delta L}(k)=\ 0.9205\), demonstrates considerable form deviations in its line profile. The results show that the line profile of steel deviates noticeably from the ideal form, indicating potential challenges in applications that require high precision and accuracy.  
In summary, the line profile analysis demonstrates the least deviations in nylon, the strong correlation between nylon and aluminum, and the highest form of delrin deformation. The correlation with changing machining conditions emphasizes the need for a comprehensive understanding of material properties and machining parameters to achieve the desired line profile tolerances in various applications.
# Infrared Stripe Correction Algorithm Based on Wavelet Analysis and Gradient Equalization
## 5 Experiment and Analysis
### Time Consumption  
We considered the time consumption of different correction methods to examine their computation complexity. The experimental environment was Matlab 2016a, Intel core i5 CPU (3.40 GHz) and 8 GB RAM. We have calculated the running time of different methods for correcting infrared images. The statistical results are listed in Table 8. Compared with several other algorithms, the proposed method requires a minimum computing time. When the complexity of the scene increases, the time required for the proposed algorithm is basically stable at a very low level. This implies that our algorithm has the potential to be applied in hardware circuit systems.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sequence/Method** & **Suitcase** & **Leaves** & **People** \\ \hline TV & 0.053 & 0.036 & 0.028 \\ MHE & 0.287 & 0.424 & 0.124 \\ CNN & 0.183 & 0.228 & 0.019 \\ Proposed & **0.168** & **0.016** & **0.012** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Average vertical gradient error (AVGE) values of correction algorithms for simulated images.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\(\eta\)} & \multicolumn{4}{c}{**Ceramic**} & \multicolumn{4}{c}{**Cameraman**} \\ \cline{2-10}  & **Noise** & **TV** & **MHE** & **CNN** & **Ours** & **Noise** & **TV** & **MHE** & **CNN** & **Ours** \\ \hline
0.02 & 12.34 & 10.39 & 8.46 & 8.32 & **7.42** & 14.10 & 12.30 & 11.06 & 10.79 & **6.42** \\
0.04 & 15.62 & 12.94 & 9.47 & 9.20 & **8.07** & 16.37 & 13.60 & 12.45 & 12.21 & **8.94** \\
0.10 & 18.27 & 14.18 & 11.46 & 11.07 & **9.72** & 18.79 & 16.28 & 14.91 & 14.27 & **10.67** \\
0.15 & 20.34 & 15.81 & 13.87 & 13.14 & **10.63** & 21.42 & 18.70 & 15.74 & 14.95 & **11.34** \\
0.20 & 25.81 & 19.76 & 15.27 & 14.35 & **11.20** & 27.41 & 21.40 & 18.69 & 18.07 & **15.13** \\ \hline \hline \end{tabular}
\end{table}
Table 5: \(\rho\) values of correction algorithms for simulated images.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{\(\eta\)} & \multicolumn{4}{c}{**Ceramic**} & \multicolumn{4}{c}{**Cameraman**} \\ \cline{2-10}  & **Noise** & **TV** & **MHE** & **CNN** & **Ours** & **Noise** & **TV** & **MHE** & **CNN** & **Ours** \\ \hline
0.02 & 12.34 & 10.39 & 8.46 & 8.32 & **7.42** & 14.10 & 12.30 & 11.06 & 10.79 & **6.42** \\
0.04 & 15.62 & 12.94 & 9.47 & 9.20 & **8.07** & 16.37 & 13.60 & 12.45 & 12.21 & **8.94** \\
0.10 & 18.27 & 14.18 & 11.46 & 11.07 & **9.72** & 18.79 & 16.28 & 14.91 & 14.27 & **10.67** \\
0.15 & 20.34 & 15.81 & 13.87 & 13.14 & **10.63** & 21.42 & 18.70 & 15.74 & 14.95 & **11.34** \\
0.20 & 25.81 & 19.76 & 15.27 & 14.35 & **11.20** & 27.41 & 21.40 & 18.69 & 18.07 & **15.13** \\ \hline \hline \end{tabular}
\end{table}
Table 5: \(\rho\) values of correction algorithms for simulated images.
## 5 Results and Discussion  
We present a comprehensive analysis of our findings, highlighting several vital aspects. These aspects include a detailed comparison with traditional flat-plate thermoelectric generators, outcomes from simulation experiments, and an evaluation of the TTEG's performance under various conditions. This section aims to elucidate the enhanced capabilities of the TTEG and potential applications in energy recovery. Here are the main findings:  
1. Comparison of TTEG with FTEG: The study compares the performance of the TTEG with traditional Flat-Plate Thermoelectric Generators (FTEGs), demonstrating a 62.5% increase in output power for the TTEG under similar conditions.
2. Simulation Experiment Outcomes: The simulation experiments for the TTEG show significant increases in output power due to altered thermoelectric element shapes and increased contact area with the heat source.
3. Temperature Variation Analysis: The study assesses the TTEG's performance under varying temperatures at the hot and cold sides, identifying the optimal operating conditions.
4. Analysis of Thermoelectric Elements: This research investigates the effect of varying the number of thermocouples in the TTEG, leading to insights into the optimal number for maximum power output.
5. Real-World Application Potential: The results suggest the TTEG's high effectiveness in practical applications, particularly in automotive and industrial environments for waste heat recovery.  
For analysis, it is assumed that every TTEG within the shell and tube heat exchanger undergoes equal heating. Therefore, the approach explicitly examines a solitary TTEG. This section initially examines the impact of different external loads on the output power of the TTEG. Subsequently, the study investigates the correlation between external load, output power, and thermal efficiency by modifying the inlet temperatures of the hot and cold sides of the TTEG under varying temperature circumstances. In addition, by maintaining consistent fluid temperatures and mass flow rates at both the hot and cold ends, this research expands the number of thermocouples within the TTEG. This is achieved using simulations and statistical analysis to identify the ideal number of thermocouple pairs for achieving the highest possible output power.
## 2 Related Work
### Latency-Aware and Fog-Edge-Cloud Architectures  
To mitigate latency and reduce bandwidth bottlenecks in IoT systems, several frameworks have adopted fog and edge computing architectures. These systems push computation closer to the data source, thereby reducing reliance on cloud infrastructure. [PERSON] et al. [PERSON] et al. (2018) proposed a latency-sensitive model for healthcare, incorporating fog, mist, and cloud layers. Their approach suffers from scalability limitations and introduces network overhead due to redundant processing. [PERSON] et al. [PERSON] et al. (2019) introduced a hierarchical scheduling policy for delay-sensitive tasks; however, this scheme lacks predictive traffic models. Likewise, [PERSON] et al. [PERSON] et al. (2019) proposed an SDN-enabled fog framework focused on architecture flexibility; however, they did not model rate adaptiveness or energy-aware behavior, both critical in healthcare IoT.  
[PERSON] et al. propose the JANUS system [PERSON] et al. (2017) for stream prioritization and latency-aware scheduling for IoT streaming; however, they only focus on bandwidth improvements and lack proactive adaptation. Similarly, [PERSON] et al. [PERSON] et al. (2018) propose an energy-efficient task offloading mechanism in fog-cloud setups; however, their model is tuned primarily for energy and load balance and does not support dynamic service-specific QoS demands. These architectures highlight the benefits of multi-tier deployment; however, they lack holistic QoS optimization, particularly under real-time, dynamic workloads.
## 3 Encoding by BFS
Gaps, the special character \(\Sigma\) (\(\Sigma\) is an integer that does not appear as a gap) and other integers are encoded using the ad-hoc \(\pi\)-code described in the next section. When a gap \(g\) could be negative (as with degrees), then we encode \(2g\) if \(g\) is positive, and \(2|g|-1\) when \(g<0\). Let \(n\) be a positive integer, \(b\) its binary representation and \(h=1+\lfloor\log_{2}(n)\rfloor\). Having fixed a positive integer \(k\), we represent \(n\) using \(k+h+\lceil\frac{h}{2^{k}}\rceil-1\) bits. Specifically, say \(h=2^{k}l-c\) (\(l>0\) and \(0\leq c<2^{k}\)), then the \(\pi_{k}\)-encoding of \(n\) is produced by writing the unary representation of \(l\), which is followed by the \(k\) bits needed to encode \(c\), and finally by the rightmost \(h-1\) bits of \(b\). For instance, the \(\pi_{2}\)-encoding of 21 is \(01\ 11\ 0101\): since the binary representation \(b\) of 21 is \(10101\) and we can write \(h=2^{2}\cdot 2-3=5\), then the prefix of the encoding is the unary representation of \(l=2\), the \(2\) bits that follow indicate the value of \(c=3\) and the suffix is formed by the \(h-1\) least significant digits of \(b\). In the following we use the approximation \(H_{P}=E_{P}(\log_{2}n)\) described in [17], where \(H_{p}\) is the entropy of a distribution with probability \(P\). The expected length for \(n\) is:  
\[E_{P}(L_{\pi}) \leq E_{P}\left(k+h+\left\lceil\frac{h}{2^{k}}\right\rceil-1\right)\] \[\leq 1+k+\frac{1}{2^{k}}+\left(1+\frac{1}{2^{k}}\right)E_{P}(\log_{ 2}n)\] \[\leq 1+k+\frac{1}{2^{k}}+\left(1+\frac{1}{2^{k}}\right)H_{P}\]  
A code is _universal_[17] if the expected codeword length is bounded in the interval \(0\ <\ H_{P}\ <\ \infty\). Each \(\pi\)-code is universal according to:  
\[\frac{E_{P}(L_{\pi})}{\max\{1,H_{P}\}}\leq k+2\bigg{(}1+\frac{1}{2^{k}}\bigg{)}\]  
and, for \(k\rightarrow\infty\), it is also _asymptotically optimal_[17], that is:  
\[\lim_{k\rightarrow\infty}\lim_{H_{P}\rightarrow\infty}\frac{E_{P}(L_{\pi})}{ \max\{1,H_{P}\}}=\lim_{k\rightarrow\infty}\left(1+\frac{1}{2^{k}}\right)=1\]  
In the context of Web Graph compression we use a modified version of \(\pi\)-codes in which 0 is encoded by 1 and any other positive integer \(n\) is encoded with a 0 followed by the \(\pi\)-code of \(n\).
## 5 Simulation Results
#### Price-Based Algorithm vs. Centralized Approach  
Herein, we compare the proposed distributed algorithm with a centralized approach to solving the BS assignment problem. In particular, we analyze the performance achieved by each algorithm in terms of the sum throughput in the system, and also estimate the execution cost in terms of the total iterations that are required to provide a BS assignment solution. The obtained results are discussed in the sequel. We compared the pricing-based algorithm's performance with the centralized BS assignment algorithm presented in. Both of the algorithms achieve similar behavior in terms of the average sum throughput, as observed in Figure 8. As mentioned before, our proposed solution only requires local information at each BS to perform the assignment process, contrary to the centralized approach in which the algorithm requires the complete network state information to decide the assignment and the resource distribution.  
Figure 8: Average sum throughput per BS, where the system was configured with five devices (on average) uniformly distributed over each cell and with a \(R_{c}^{\min}\) requirement of 250 Kbps.  
Figure 7: CDF of the data rate degradation for a \(R_{c}^{\min}\) requirement of 600 Kbps for HTC devices and \(R_{d}^{\min}\) of 30 Kbps for MTC devices. Also, the number of devices was set to 12 HTC devices and 25 MTC devices per cell. The system backhaul was considered to be limited with a backhaul capacity of 12 Mbps.  
Figure 9 presents an analysis of the number of iterations obtained by each algorithm under a variation of the number of devices per cell. The results have shown that our proposed solution requires fewer iterations to perform an update of the prices per resource unit. For instance, in the configuration of four devices per cell, our proposed approach requires an average of 2.5 iterations, while the centralized algorithm requires an average of 5.8 iterations. This means a reduction in the required number of iterations for about 56%, because of the centralized approach update at each iteration, the prices of only one BS. In contrast, in our solution, each BS independently updates its prices at each iteration.
# The Cluster Computation-Based Hybrid FEM-Analytical Model of Induction Motor for Fault Diagnostics
## 1 Introduction
The basic building blocks of the cloud computation are infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). Those building blocks can be utilized for big data storage, custom software development, and computer application utilization respectively. In order to curtail the complexity related problems of FEM models, the concept of parallel processing by utilizing the cluster of computers is presented in this paper. Unlike most of the papers where the simulation speed of FEM models is increased either by exploiting the symmetry (which is not true in the case of faulty machines) or by data interpolation, in this paper the complete two-dimensional (2D) geometry of a three-phase squirrel cage induction motor is solved on multiple processor cores working in parallel with each other. All inductances are calculated by doing a magneto-static solution of the machine at several rotor positions. The calculated inductances are saved in the three-dimensional (3D) lookup table as a function of the rotor position. The dynamic behavior is then studied in MATLAB/Simulink, and the results are validated by comparing them with the measurements taken from the laboratory test rig.
## 3 Methodology
### F-Svd  
The interpolation method adopted in this study is a spatiotemporal precipitation method based on matrix decomposition (hereafter referred to as F-SVD) proposed by [PERSON] et al. [24], which has been proven to outperform the traditional interpolation methods (inverse distance weight, ordinary kriging) through cross-validation and offer a better spatial estimation. The calculation process is presented in Figure 2a and contains the following steps:  
1. The historical precipitation information from \(m\) surrounding gauges and \(n\) past moments needs to be prepared for the interpolation at the target point at a certain moment.
2. The precipitation data from the surrounding gauges and the target point from adjacent moments can form a spatiotemporal data matrix with dimensions of \((m+1)\times n\), where the rows represent time and columns represent space.
3. If any precipitation values in the matrix representing the historical precipitation of the target point are unknown, traditional interpolation methods such as IDW should be used to calculate these values until only one null value representing the precipitation to be estimated remains.
4. The F-SVD method is utilized to decompose the matrix into a temporal feature matrix \(X\) and a spatial feature matrix \(Y\). The stochastic gradient descent algorithm is used for optimization.
5. Then, the two optimal feature matrices are multiplied to reconstruct a matrix \(P\), the element at the \(m+1\) row and \(n\) column in the reconstructed matrix represents the estimated precipitation, which is calculated as follows: \[P_{i,j}=\sum_{q=1}^{q}X_{i,q}Y_{q,j}\] (1) where \(q\) is the number of latent features.
# An Adaptive Process Monitoring Method Based on an Improved ADALINE Neural Network
## 3 Adaptive Process Monitoring Approach
### Control Limit Adaptive Model  
#### 3.3.1 Determination of Initial Control Limits  
In the repeat machining process, for one of the machining cycles, the monitoring characteristic variable is selected, and \(K\) is the number of sample points to form a time series data record as \(X_{i}(1,2,\cdots,k)\), assuming there are \(I\) cycles. \(X_{i}(I\times K)\) is the data block, which is expanded according to the operation cycle. The process is shown in Figure 5. The original data were preprocessed and prepared for determining control limits.  
The initial control limit only needs rough monitoring for the machining process. \(3\sigma\) is one of the simplest classical methods for process monitoring and can be used to set up the initial control limit. And the value \(\mu\) and variance \(\sigma\) can be calculated using Equation (6) and Equation (7), respectively:  
\[\mu=\lim_{A\rightarrow\infty}\frac{1}{A}\sum_{i=1}^{A}f_{ij}(k), \tag{6}\]  
\[\sigma=\lim_{A\rightarrow\infty}\sqrt{\frac{1}{A}\sum_{i=1}^{A}{(f_{ij}(k)- \mu)^{2}}}, \tag{7}\]  
where \(f_{ij}(k)\) is the measured value of the \(j\)-th monitoring variable in the \(i\)-th cycle. \(A\) is the number of cycles selected for the test. The upper and lower control limits are \(\mu\pm 3\sigma\), so the initial control limit is obtained.  
Figure 5: Schematic diagram of initial control limit determination.
# Applications of Wireless Sensor Networks: An Up-to-Date Survey
## 2 Main WSN Applications
### Health Applications
#### 2.2.2 Home Assisting Systems  
The authors of [15] presented a homecare monitoring platform with internet remote connection to assisted people and their environment. This platform can monitor and diagnose patients remotely, in real time in their home environment by the utilization of wearable or even surgically inserted biosensors. A hybrid model of the platform is proposed, with various levels in terms of functionality, combining fixed and mobile nodes. These levels range from simple data acquisition of the assisted person to primary care and emergency nodes, up to the communication and coordination with an appointed help center as a hospital.  
During their research, the authors of [16] investigated real-time sensors for the diagnosis of cardiac patients by using smartphone and wearable sensors. The authors introduced this application to cater a remote real time monitoring for severe cardiac patients unable to attend a routine checkup. One of the main reasons behind this technology is to commercialize it for the benefit of those patients who are not financially sound. Furthermore, the system was developed in such a manner that any kind of crisis phase is dealt with in terms of alerting messages that are automatically sent to the doctor. An overview of the operation of this system is shown in Figure 8.  
Figure 8: The system configuration of the remote patient monitoring application [16].  
Figure 7: An illustration of the real time electrocardiogram (ECG) monitoring environment [13].
## 6 Smart Transportation
### The Optimization of Parking Lot Utilization  
To enhance parking lot usage, scholars tend to develop an IoT-based application to provide parking availability information and guide parking routes. [PERSON] et al. [PERSON] et al. (2018) introduced COMPOSE, an open-source platform as a service, to develop the IoT transportation systems in Barcelona. This system collects the data of users' position by GPS and the data of parking with parking sensors in the city. [PERSON] et al. [PERSON] et al. (2018) developed a GS1 smart parking system to deal with the sensor-generated data in the parking scenario. This system consists of EPC Information Service, Object Name Service, and Area-2-GLNs mapping service. This system has been used in Busan City and nine Korean airports. [PERSON] et al. [PERSON] et al. (2018) presented a global smart parking platform covering the IoT smart parking systems in five smart cities, including Barcelona, Santander, Seoul, Busan, and Seongnam. There are two types of parking sensors used in these cities. One type of parking sensor is based on ferromagnetic detection, which is buried under the asphalt. They can record the percentage of time a vehicle is on top of the magnetic loop. These sensors also can record the total counted vehicles per hour and a traffic congestion index, as well as model traffic congestion. Another type of parking sensor is based on radar sensing, which is mounted over the floor. The sensors communicate the data through a low-power wide-area network.  
Regarding data analysis for IoT smart parking systems, [PERSON] et al. [PERSON] et al. (2018) found that a regression tree is the least computationally intensive algorithm that performs the best for parking availability prediction in the real-time car parking datasets of San Francisco City and Melbourne City. [PERSON] and Yu [PERSON] and [PERSON] (2018) used a three-axis AMR sensor to generate magnetic signals and applied normalized cross-correlation (i.e., k-means clustering algorithm) as their data analysis method, improving the detection accuracy for on-street parking. [PERSON] et al. [92] used a discrete Markov chain model to propose the SmartPark algorithm to analyze the data of an IoT parking system and reveal the future state of the parking lot when vehicles are expected to arrive at the parking lot.
## 2 Methods
### Improved Grey Wolf Optimizer (IGWO)
#### 2.2.2 Nonlinear Convergence Factor  
Introducing a nonlinear approach to modulating the convergence factor significantly boosts the algorithm's proficiency in both global and local searches, thereby enhancing its efficiency in finding solutions. Traditionally, the Grey Wolf Optimizer (GWO) algorithm employs a linearly decreasing convergence factor a, which reduces with each iteration. This linear reduction might not adequately cater to the diverse demands of different search phases, occasionally resulting in premature convergence or entrapment in local optima [33]. By adopting a nonlinear function for adjusting a, the algorithm gains the flexibility to adapt its convergence rate in response to the evolving dynamics of the search process, as shown in Figure 3.  
\[a=\cos\left(\frac{\pi}{2}\times\left(\frac{1-t}{t_{max}}\right)\right) \tag{12}\]  
where \(t\) denotes the current iteration count within the optimization process, and \(t_{max}\) specifies the upper limit on the total number of iterations allowed.