esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Simulations and Discussion
### Finding of Adjustment Point
#### 3.2.2 Discussion  
In the first kind of numerical simulation, the unbalance responses calculated by CR-DAM are directly put into Algorithm I and Algorithm II to identify the bearing coefficients.  
In the simulation of g4.4 based on Algorithm I, the relative identification errors of #1 bearing's main coefficients are almost equal to zero, while the identification errors of other bearings can be very big when #14 node near #1 bearing is used as the last measuring point. Moreover, when #29 node (near #2 bearing), #33 (near #3 bearing), #49 (near #4 bearing), #53 (near #5 bearing), #69 (near #6 bearing), #73 (near #7 bearing) and #91 (near #8 bearing) are used as the last measuring points, respectively, the relative identification errors of #2, #3, #4, #5, #6, #7 and #8 bearing's main coefficients are almost equal to zero, respectively.  
Moreover, the same rule can be obtained according to the simulation results of g4.4 and h4.4 based on Algorithm II. It is indicated that the measuring points in Tables 1 and 2 can adjust the identification error. If the measuring point is near a bearing, the identification accuracy of the bearing is high. Hence, the points in Tables 1 and 2 are called adjustment points.  
In addition, the identification errors of main stiffness coefficients are smaller than that of the main damping coefficients in the simulation based on Algorithm I. This is because the main stiffness coefficients are far greater than the main damping coefficients for a rolling bearing. The numerical calculation errors of computers, such as rounding error and calculation accuracy, have little influence on big numbers and a great influence on small numbers.  
In the simulations of g4.4 based on Algorithm II, the identification errors of main stiffness coefficients are also smaller than that of the main damping coefficients. It is also because the main stiffness coefficients are much bigger than the main damping coefficients, and the numerical calculation errors have little influence on big numbers and a great influence on small numbers. However, the identification errors of cross-coupled stiffness coefficients are bigger than that of the cross-coupled damping coefficients. The cross-coupled stiffness and damping coefficients are equal and are nearly zero in g4.4. This indicates that when the difference between the cross-coupled stiffness coefficient and cross-coupled damping coefficient is small, the identification errors of the cross-coupled stiffness coefficients are bigger than those of the cross-coupled damping coefficients.  
Similarly, the identification errors of damping coefficients are also smaller than those of the stiffness coefficients in the simulation of h4.4 based on Algorithm II. This is because the difference between the stiffness coefficients and damping coefficients is not particularly big. It can be inferred that the damping coefficients of journal bearings can be better identified than the stiffness coefficients by using Algorithm II.
# Herbicide Bioassay Using a Multi-Well Plate and Plant Spectral Image Analysis
## 2 Materials and Methods
### Spectral Image Acquisition and Analysis
#### 2.3.2 CF Images  
Chlorophyll fluorescence (CF) images of the crabgrass plants in well plates were acquired using an SNU-KIST 2 imaging system (Seoul National University/KIST, Seoul/Gangneung, Republic of Korea) at 3, 6, 24, 48, 72, and 120 HAT. For each CF image, the pixel intensity of the crabgrass was averaged to derive the CF value after removing the background by Otsu thresholding. The CF image parameters were then calculated using the following formulae:  
\[\mathrm{F_{v}/F_{m}}=\frac{F_{m}-F_{0}}{F_{m}} \tag{4}\]  
\[\mathrm{\PhiPSII}=\frac{F_{s}-F_{0}}{F_{s}} \tag{5}\]  
\[\mathrm{F_{d}/F_{m}}=\frac{F_{m}-F_{s}}{F_{m}} \tag{6}\]  
\begin{table}
\begin{tabular}{c} Farmhannong Ltd., Seoul, \\ Republic of Korea \\ \end{tabular} \\ \hline \end{tabular}  
* PSI: photosystem I, PPO: protoporphyinogen oxidase, ALS: acetlactate synthase, HPPD: 4-hydroxyphenylpyruvate dioxygenase, GS: glutamine synthase, EPSPS: 5-enolpyruvate/Nishclimate-3-phosphate. 2 SL: soluble concentrate, ME: microemulsion, SC: suspension concentrate, WG: water dispersible granule.  
\end{table}
Table 1: List of herbicides used for herbicide bioassay using well plates and spectral image analysis.  
where \(F_{0}\) is the ground CF at the dark-adapted state, \(F_{m}\) is the CF value at 1 s when a plant emits maximum fluorescence, and \(F_{s}\) is the CF value at 60 s when it comes to a steady state. The CF parameters were then normalized by dividing the values of the crabgrass treated with herbicides by those of the untreated control.
# Future of IoT Networks: A Survey
## 3 Current Market and Research Trend of IoT
### Research Programs
#### 3.2.3 Asia  
In terms of Asia, China and Japan are the major nations that are currently investigating IoT vigorously.  
China has gathered 5 billion Yuan as a fund for Chinese IoT investment. The Chinese government's latest five-year plan primarily chose the smart grid, transportation, logistics, the home, environment and security, industry and automation, health, agriculture, finance, and military as principal service areas for developing IoT technologies [33; 34]. In addition, the Chinese government has placed emphasis on a nation wide standardization strategy. Moreover, the Chinese government is establishing a demonstration test bed and IoT database. The Chinese government established a project named as \"Internet Plus\" strategy [35], which is a plan to collaborate between IoT technology and manufacturing business.  
Along with the Chinese government, Japan is currently conducting the \"i-Japan Strategy 2015\" research program as a part of IoT research [36]. The Ministry of Internal Affairs and Communications (MIC) of Japan created a consortium that is based on mobile carrier service providers and system vendors [37]. The Japanese government is concentrating on remote health-care and earthquake monitoring to enable a secure future digital society. The Ministry of Education, Culture, Sports, Science and Technology ordered the new project, \"The Research Project for Information & Communication Technology (ICT) System Architecture in the Era of IoT\" [38] to get a foothold in IoT environment.
# ALPINE: Active Link Prediction Using Network Embedding
## 4 Query Strategies for ALPINE
### Uncertainty Sampling  
Uncertainty sampling is perhaps the most commonly used query strategy in active learning [14]. It selects the least certain candidate to label, and entropy is widely used as the uncertainty measure. In active network embedding for link prediction, a node pair with an unknown link status can be labeled as unlinked (zero) or linked (one). According  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Strategy** & **Definition** & **Utility Function** \\ \hline
**page-rank.** & PageRank score sum & \(u_{A}(i,j)=\mathrm{PR}_{i}+\mathrm{PR}_{j}\) \\ \hline
**max-deg.** & Degree sum & \(u_{A}(i,j)=\sum_{k:(i,k)\in E}a_{ik}+\sum_{l:(j,l)\in E}a_{jl}\) \\ \hline
**max-prob.** & Link probability & \(u_{A,X^{*}}(i,j)=P(a_{ij}=1|X^{*})\) \\ \hline
**min-dis.** & Node pair distance & \(u_{A,X^{*}}(i,j)=-||x_{i}^{*}-x_{j}^{*}||_{2}\) \\ \hline
**max-ent.** & Link entropy & \(u_{A,X^{*}}(i,j)=-\sum_{a_{ij}=0,1}P(a_{ij}|X^{*})\log P(a_{ij}|X^{*})\) \\ \hline
**d-opt.** & Parameter variance reduction & \(u_{A,X^{*}}(i,j)=u_{X^{*}_{j}}(i,j)+u_{X^{*}_{j}}(i,j)\) \\ \hline
**v-opt.** & Prediction variance reduction & \(u_{A,X^{*}}(i,j)=\sum_{k:(i,k)\in T}u^{ik}(i,j)+\sum_{l:(j,l)\in T}u^{ll}(i,j)\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the query strategies for ALPINE.  
to the link probabilities obtained from the learned network embedding model, the entropy of a node pair's link status is:  
\[u_{\mathbf{A},\mathbf{X}^{*}}(i,j)=-P^{*}_{ij}\log(P^{*}_{ij})-(1-P^{*}_{ij})\log(1-P^{*} _{ij}),\]  
where \(P^{*}_{ij}=P(a_{ij}=1|\mathbf{X}^{*})\). It defines the **max-ent.** strategy that selects the most uncertain candidate link status to be labeled by the oracle. Intuitively, knowing the most uncertain link status maximally reduces the total amount of uncertainty in the unobserved part, although indirect benefits of the queried link status on the model's capability to predict the status of other node pairs are not accounted for.
## 4 Verification with Practical Results
The pulse non-regulated topology is very simple to realize, and its input current quality is inherently improved as its flow is continuous. Figure 16: Practically recorded results for one-third output frequency with pulse voltage regulation capabilities: (**a**) output voltage and its FFT spectrum; (**b**) input current and its FFT spectrum.
# Analysis and Design of A PMQR-Type Repetitive Control Scheme for Grid-Connected H6 Inverters
## 4 Simulation Results and Analysis
### Controller Design
#### 4.1.2 The Internal Model \(1/\left(1-Q\left(z\right)z^{-N}\right)\)  
The value of carrier frequency (PWM switching frequency) was equal to the sampling frequency, which was set at 20 kHz, and \(f_{g}\) = 50 Hz. Therefore, the cyclic delay link was \(z^{-N}=z^{-400}\). \(Q\left(z\right)\) improves robustness by attenuating the integral action slightly, which can be a constant less than one. As shown in Figure 11, the internal model of RC has a high gain at the fundamental and multiple frequency together with no phase shift existing. However, the closer \(Q\left(z\right)\) is to one, the worse the system stability becomes. After comprehensive consideration, \(Q\left(z\right)=0.95\) is selected in this paper.  
Figure 10: Bode plot of \(P_{0}\left(z\right)\) under different \(k_{p}\).  
Figure 9: The dominant pole assignment map of \(P_{0}\left(z\right)\) under different \(k_{p}\).
# Elliptic Curve Signcryption-Based Mutual Authentication Protocol for Smart Cards
## 6 Analysis of Security Functionalities of ECSSP-SC
### Analysis of Resistance Capability of ECSSP-SC against Attacks
#### 6.2.1 Resistance to Replay Attacks  
An _Attacker_\(A\) resides between the \(USR\) and \(SVR\) to record the messages \(\{c,T,w,\ T_{U}\}\) and \(\{A_{S}.T_{S}\}\) in a replay attack. The attacker then replays these messages later to the \(SVR\) or \(USR\), respectively, to yield an illicit effect. In the ECSSP-SC, the current timestamp has to be provided in both the messages. Thus, if an _Attacker_\(A\) tries to replay the past recorded message \(\{c,T,w,\ T_{U}\}\) to the \(SVR\), then, \(T_{C}-T_{U}>t\) and the \(SVR\) will reject it. Similarly, if an _Attacker_\(A\) tries to replay the past recorded message \(\{A_{S}.T_{S}\}\) to the \(USR\), then \(T_{C}-T_{S}>t\), and the \(USR\) will reject it. In addition, the fresh private random number \(v\) is used by the \(USR\) in the computation of the key \(K\), and if the attacker replays the recorded message to fool the \(SVR\) or \(USR\), it will be detected easily, since the key \(K\) or \(K^{\prime}\) generated will be different and the authentication will fail. In this way, the proposed ECSSP-SC offers security from replay attacks.
## 5 Applications of PCMs in Buildings
### Concrete Blocks and Bricks  
Another technology that has been investigated is the incorporation of PCMs into building elements such as concrete blocks and bricks. Vinh Duy Cao et al. [91] incorporated microencapsulated PCMs in Portland cement and in geopolymer concrete. The results obtained from thermal and mechanical analysis show that latent heat capacity increases, while thermal conductivity and compressive strength decreases, in proportion with the conciseness of PCMs. The addition of 3.2 wt.% and 2.7 wt.% to Portland cement and geopolymer concrete, respectively, can reduce the power consumption of the building in 23 \({}^{\circ}\)C by 11% and 15%, respectively. PCMs can also be successfully incorporated in bricks. Rajat Saxena et al. [92] integrated eicosane and OM35 PCMs into bricks. The results show an essential reduction of temperature fluctuation (4.5 \({}^{\circ}\)C to 7 \({}^{\circ}\)C) between incorporated and conventional bricks. A recent study from Mahmudul Hasan Mizan et al. [93] investigated the enhancement of interfacial bonding between concrete and PCM. The experiment included the preparation of 36 specimens with different surface preparation techniques each. The strength improvement was achieved by using silica fume.
###### Abstract  
For Gaussian noise with random or periodic impulsive interference, the conventional active noise control (ANC) methods with finite second-order moments may fail to converge. Furthermore, the intensity of impulsive noise typically varies over time in the actual application, which also decreases the performance of conventional active impulsive noise control methods. To address these problems, a novel adaptive state detector based post-filtering active control algorithm is proposed. In this work, information entropy with adaptive kernel size is first introduced into the cost function of a post-filtering algorithm to improve its tracking. To enhance the robust performance of adaptive filters when impulsive interference happens, a recursive optimal threshold selecting method is also developed and analyzed by statistical theories. Simulations show that the new method has fast tracking ability in non-impulsive noise environment and keeps robust when impulsive interference happens. It also works well for the impulsive noise of different degrees. Experiment results confirm the effectiveness of the proposed algorithm.  
active impulsive noise control; post-adaptive filtering; information entropy; state detector +
Footnote †: journal: Applied Physics Letters
# Efficient Dynamic Cost Scheduling Algorithm for Financial Data Supply Chain
## 5 Sensitivity Analysis
#### 5.1.4 Changing the Processor Rental Costs  
When increasing the renting cost of an additional processor to 2.5 times the leased processor cost, the program rented the same number of processors to finish the batch process because the penalty cost is still higher relatively. It only ended up increasing the total batch cost, while the same number of additional processors were utilized. Increasing the processors renting cost to 5 times the leased processor cost did not stop the program from renting additional processors to finish the batch process because the penalty cost is still relatively higher. However, the total batch cost increased while the same number of additional processors were utilized. To prove the efficiency of the algorithm, the case of equal costs for leased and rented processors was tested, the algorithm rented only what it needed from available additional processors to finish the batch process. Finally, in the case of setting costs ratio to 0.5, the total price went lower but the files-to-processors allocation stayed the same.  
The above findings and analysis prove that when the algorithm reaches the optimum solution, it does not utilize any unnecessary resources since it is part of the objectivefunction to minimize all types of costs while trying to meet SLA deadline and satisfy all priorities and constraints.
# Satellite-Based Localization of IoT Devices Using Joint Doppler and Angle-of-Arrival Estimation
## 3 System Model
### Constellation Geometric Model  
Although the proposed framework is not restricted to a particular geometric setup, we consider the common circular polar orbit because of its near-uniform distance to the Earth's surface throughout its orbital period. For the constellation model, we adopt the popular Walker-star pattern which is widely used in telecommunication applications. The Walker-star pattern has three key parameters [29]: (i) the total number of satellites \(N\), (ii)the number of orbital planes \(P\), and (iii) the phasing parameter \(F\). The phasing parameter is defined as an integer within \(0\leq F\leq(P-1)\) representing the relative offset between satellites in two subsequent orbital planes. In the Walker-star constellation, the orbital planes are uniformly rotated around the Earth's axis; thus, the right ascension of the ascending nodes (RAAN) for an orbital plane \(j\in[1,P]\) is given by [30],  
\[\Omega_{j}=\frac{\pi}{P}(j-1), \tag{1}\]  
and the initial true anomaly \(\
u\) is given by [31],  
\[\
u_{jl}=\frac{2\pi}{S}(l-1)+\frac{2\pi F}{N}(j-1), \tag{2}\]  
where \(l\) is the order within the orbital plane, i.e., \(l\in(l,S)\), and \(S=N/P\) is the total number of satellites on the orbital plane. An example of the satellite orbits following the Walker-star pattern is shown in Figure 2.
## 3 SB as SE Process--Review and Discussion
### Correlations in the Integrated SE Approach
#### 3.2.2 Correlations in Studies Conducted  
The conducted studies [36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49] (reference [85] is devoted to a strategy for the production of PCD deforming inserts) are classified into five categories according to the correlations investigated in them (Figure 14): (1) examination of only the simple correlation SB-SI (67%); (2) examination of only the simple correlation SB-OB (8%); (3) parallel study of the two simple correlations SB-SI and SB-OB (21%); (4) SB-SI-OB correlation study (1%); (5) investigation of the correlation SB-(SI + OB) (3%) and multi-objective optimization (whereby the vector optimization criterion contains characteristics (objective functions) of both SI and OB) with the goal of synthesis of an optimal SB process. As can be seen from Figure 14, most studies are devoted to the simple SB-SI correlation, and the detailed information is systematised in Table 6.  
Figure 13: Components of SE, correlations between them, and optimizations.  
Several studies are devoted to the direct correlation SB-OB: [PERSON] et al. [37] investigated the influence of the burnishing force in SB of shoulder fillets on the fatigue behaviour (S-N curves) of steel shafts; [PERSON] et al. studied the wear rate of tool steels after hard turning and subsequent DB [43], DB and subsequent sulphonitriding [40], DB and vacuum nitriding [42], and DB and nitriding [44]. The governing factor in DB process was the burnishing force. Table 7 contains detailed information about the parallel study of the two correlations DB-SI and SB-OB.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7  
The SB-SI-OB correlation was studied by [PERSON] at al. [36]. The effect of SB force and the deforming element material on the SI and wear rate of 42 CrMo4 steel was investigated, and, subsequently, the effect of surface topography parameters on the wear rate.  
[PERSON] et al. [70; 72] modelled and optimised the DB of 41 Cr4 steel in order to synthesise an optimal DB process, whereby the vector optimization criterion contained characteristics (objective functions) of both SI and OB. The studied correlation can be denoted as SB-(SI + OB).
## Materials and Methods
### Statistical Methods and Data Analysis  
First, we examined the internal correlations within the field-measured crop parameter data to decide upon the potential limitations of the analyses. Next, we calculated the Kendall's rank correlation coefficient (\(\tau_{k}\)) between LAI and the selected VIs from both simulated and field-measured data. Kendall's \(\tau_{k}\) is a non-parametric measure of the strength of a monotonic relationship between paired data. The value of \(\tau_{k}\) lies between \(-1\) and \(1\), with \(\tau_{k}=-1\) indicating a perfect negative correlation between the paired data, \(\tau_{k}=0\) the lack of a relationship and \(\tau_{k}=1\) a perfect positive correlation. We chose \(\tau_{k}\) instead of the more standard Pearson's correlation coefficient \(R\) (and the related coefficient of determination \(R^{2}\)) because the field data did not satisfy the assumption of normality. Neither did we have to assume a linear relationship between the vegetation parameters and VIs. Despite similar ranges, the numerical value of \(\tau_{k}\) for a relationship between any two variables is generally different from \(R\).  
To determine how MTA affects the performance of the indices in estimating LAI, we fixed Cab in the simulated data by extracting simulations with Cab between 45-50 \(\mathrm{\SIUnitSymbolMicro g}\) cm\({}^{-2}\). Next, we divided the simulations into groups based on MTA (15\({}^{\circ}\), 30\({}^{\circ}\), 50\({}^{\circ}\), and 70\({}^{\circ}\)) and plotted the VIs calculated from the data against LAI. Similarly, we fixed MTA at 57\({}^{\circ}\) and varied Cab between three levels (25-30, 55-60, and 95-100 \(\mathrm{\SIUnitSymbolMicro g}\) cm\({}^{-2}\)) to estimate the effect of Cab on the VI-LAI relationship. Due to the imbalance in the measured actual species-specific leaf angles caused by an uneven distribution of samples between species, we could not analyze the sensitivity of the VI-LAI relationship to MTA in the field-measured dataset.
## 3 Analysis and Discussion
### Study on the \(M_{2}\) Model  
The studies in the above sections show that the features extracted by AFEM are more applicable to the task at hand than MFCCs, allowing the validation accuracy of the model to be increased by a maximum of 2.1%. However, the complete information of the ground motion acceleration time course contains two aspects: amplitude and phase, obtained by the Fourier transform. AFEM of the \(M_{1}\) model only uses the power spectrum (amplitude) without focusing on the phase, which cannot fully represent the non-stationary nature of the ground motion acceleration time history. A study [86] has also shown that phase information is valuable in processing and analysing seismic data. Therefore, phase feature learning needs to be introduced in AFEM of the \(M_{2}\) model to optimize the performance.  
Moreover, as shown in Figure 12, we test the network performance when \(\gamma\) takes different initial values while extracting the phase features. The results indicate that the overall change in the network validation accuracy is small compared to using log activation. However, the calculation time is reduced by 14.7% within the range of values taken (as shown in Table 7). More importantly, by comparing with the amplitude features, the phase features extracted have an average improvement of 5.4% for the \(M_{0}\) model validation accuracy. Meanwhile, the verification accuracy is improved by 7.2% on average compared  
Figure 13: Magnitude response of the learned weight matrix ordered in centre frequency. (**a**) Mel initialization and learned centre frequency (AFEM); (**b**) Mel-initialized weight matrix (AFEM) linear layer; (**c**) Training weight matrix of 40 dimensions with (**right**) and without (**left**) normalization; (**d**) Training weight matrix of 100 dimensions with (**right**) and without (**left**) normalization.  
to the hand-extracted features. Here, the computational results when \(\gamma=-1/7\) are given in Table 7, which shows that the computational efficiency of the \(M_{2}\) model is significantly improved over the one which directly uses hand-crafted features as the input.  
On the other hand, both log and power functions are appropriate activation functions, as shown in Figure 14. The learned weight matrix has some variation pattern, allowing AFEM to extract more practical features. For amplitude features, a log is better than the power function to make the learned weights less noisy and more regular. On the contrary, the power function performs better for the phase features. According to the analysis from Section 3.3.4, it is clear that power-transform can obtain information over a larger region compared to log-transform. Furthermore, combined with the phase feature, the \(M_{2}\) model can extract more intensity non-stationary information from the GM data, which is more appropriate for our task. Additionally, amplitude and phase are concatenated in the feature dimension and fed into the GRU network to explore information that may be complementary to the amplitude component alone. Validation accuracy is again improved slightly.