esa-sceva/satcom-chunk-collection huggingface dataset:
# Electrically Driven Micro- and Nano-Scale Semiconductor Light Sources
## 7 Metal-Waveguide-Integrated Nano-LEDs for Surface Plasmon Polariton (SPP) Sources
They employed an electrically driven InP-based 1D PhC nanocavity and integrated it in an SOI waveguide. They achieved electrically pumped CW lasing at room-temperature. Notably, the threshold current was measured as \(\sim\)100 uA at 1 V applied voltage, which was five-fold lower than that of the microdisk laser in Figure 11. In addition, the reported device exhibited a maximum output power of \(\sim\)95 uW, coupled in the SOI waveguide, and an estimated wall-plug efficiency of up to \(\sim\)14%. Evidently, these results represent considerable improvements in many aspects of device performance compared to the previously reported electrically powered micro- and/or nano-scale lasers. Figure 11: Heterogeneous integration of electrically driven micro- and nano-scale lasers with SOI waveguides. (**a**) Schematic representation of a vertically _n-i-p_-doped III-V microdisk injection laser heterogeneously integrated with a silicon-on-insulator (SOI) wire waveguide. (**b**) Optical microscope image of integrated InP-based microdisks including InAsP quantum well active layer with Si optical waveguides before contact metallization (left). Focused ion beam cross-sectional view of one of the microdisks in the optical image indicated by the white-dotted line (middle). Magnified SEM image showing a detailed configuration of the integrated InP-InGaAsP microdisk and Si waveguide (right) [47]. Figure reproduced with permission from Optical Society of America. Two major challenges are worth mentioning for this heterogeneous integration. First, a simple and easy alignment with high precision and accuracy still need to be improved since the success of integration critically relies on this process. Second, the EM energy transfer is critically determined by how strongly or weakly the evanescent fields are formed between the laser and waveguide. Therefore, finding an optimal thickness of the deposited and planarized SiO\({}_{2}\) layer is of considerable importance.
# Reliability Analysis of Military Vehicles Based on Censored Failures Data
## 2 Methodology
### Weibull Model  
The Weibull model is a two-parameter distribution used to describe the reliability of a technical object for which the failure intensity is time-varying. The reliability function in the Weibull model [42; 46] is expressed by the relation:  
\[R(t)=e^{-\beta t^{\alpha}}. \tag{11}\]  
where \(\alpha\) is the shape factor and \(\beta\) is the scale factor of the distribution.  
After calculating the derivative from the failure function, a relation representing the failure probability density function is obtained:  
\[f(t)=\alpha\beta t^{\alpha-1}e^{-\beta t^{\alpha}}. \tag{12}\]  
After substituting the characteristics determined for the Weibull model into Equation (4), the failure intensity [47] can be expressed by the following equation:  
\[\lambda(t)=\alpha\beta t^{\alpha-1}. \tag{13}\]  
The parameter \(\alpha\) determines the course of the failure intensity function of the technical object. For \(0<\alpha<1.0\) the failure intensity function is decreasing, for \(\alpha>1.0\) it is increasing, while for \(\alpha=1.0\) it is constant and the distribution has the character of an exponential distribution.
# Development of a Multiplex PCR Assay to Monitor Living Modified Cottons in South Korea
## 2 Materials and Methods
### DNA Extraction and Polymerase Chain Reaction for Multiplex Analysis  
The genomic DNA from the CRM and samples collected from the monitoring project were extracted using the Exgene plant SV mini kit (GeneAll, Korea). The DNA concentration and purity for each sample was determined using the NanoDrop 2000 spectrophotometer (Thermo Fishier Scientific, USA).  
Each event specific primer pair was designed using genetic information and sequence data that was obtained from the Joint Research Centre-European Commission (JRC-EC) and International Service for the Acquisition of Agri-biotech Applications (ISAAA) [16,17]. The acyl carrier protein 1 (ACP1)region was used for specific endogenous genes of cotton [18]. The primer set for each event for the multiplex-PCR was designed using event specific regions.  
Polymerase chain reaction (PCR) was performed using 2 xFF-Taq PCR pre-mix (Solgent, Korea) containing 50 ng of template DNA and 50-70 ng of each primer. Multiplex PCR conditions included one cycle for 30 s at 94 \({}^{\circ}\)C (denaturation); 30 s at 65 \({}^{\circ}\)C, while reducing the temperature by 0.7 \({}^{\circ}\)C per cycle (annealing); and 1 min at 72 \({}^{\circ}\)C (extension); followed by 23 cycles for 30 s at 94 \({}^{\circ}\)C, 30 s at 56 \({}^{\circ}\)C, and 1 min at 72 \({}^{\circ}\)C [19]. The genomic DNA was amplified using the ProFlex PCR System (Applied Biosystems, USA). Amplified products were separated by electrophoresis on a 2.0% agarose gel and the gel images were visualized and analyzed using the ChemiDoc XRS+ Imaging System (Bio-Rad, USA).
# Evaluation of Network RTK Positioning Performance Based on BDS-3 New Signal System
## 2 Network RTK Positioning Algorithm
### Generation of Virtual Observations (VRS)  
The virtual observations are obtained from the observations of the main reference station and various error corrections [26; 30]. The satellite signal transmission time and atmospheric delay of the observation between the virtual reference station and physical reference station are different, and the observation of the main reference station needs to be corrected by geometric correlation terms and atmospheric delay. The steps to generate virtual observations based on the observations of the main reference station are as follows:  
\[\left\{\begin{array}{l}P_{V,k}^{p}=P_{M,k}^{p}+\rho_{MV}^{p}\\ \varphi_{V,k}^{q}=\varphi_{M,k}^{q}+\rho_{MV}^{p}/\lambda_{k}\\ P_{V,k}^{p}=P_{M,k}^{p}+P_{V,k}^{p}+\rho_{MV}^{pq}+\gamma_{k}\cdot I_{MV,1}^{ pq}+T_{MV}^{pq}\\ \varphi_{V,k}^{q}=\varphi_{M,k}^{pq}+\varphi_{V,k}^{p}+(\rho_{MV}^{pq}-\gamma _{k}\cdot I_{MV,1}^{pq}+T_{MV}^{pq})/\lambda_{k}\end{array}\right. \tag{11}\]  
where, \(P_{V,k}^{p}\) and \(\varphi_{V,k}^{p}\) represent the pseudo-range and phase virtual observation of reference satellite, while \(P_{V,k}^{q}\) and \(\varphi_{V,k}^{q}\) represent the pseudo-range and phase observation of the non-reference satellite. The derivation of the above Equation is based on the following three assumptions:  
* The ambiguity of all satellite observations of the VRS is the same as that of the main reference station.
* The clock error between the virtual reference station and the main reference station is the same; that is, the capture time of the observation of the two stations is the same.
* The ionospheric and tropospheric delays of the reference satellite of the VRS are the same as those of the main reference station.  
In addition, the generation of VRS is completely based on the observation values of the main reference station. Besides ionospheric and tropospheric errors, there is also geometric error correction. The generation of \(\rho_{V}^{p}\) and \(\rho_{V}^{q}\) related to geometric correction is an iterative process. The condition at the end of the iteration is that the satellite position is used to generate the geometric distance and the satellite position at the launch time based on the inverse calculation of \(P_{V}^{p}\) and \(P_{V}^{p}\) are less than a certain threshold (e.g., 0.01 mm). Theoretically, there will be a deviation in the observation of the generated reference satellite. However, since the deviation of all satellites is consistent, the user will eliminate the influence of this deviation in the combination of double-difference positioning, so the positioning accuracy of the terminal will not be affected [30].
# Dynamic Optimization of Buckling Problems for Panel Structures with Stiffening Characteristics
## 2 Panel Structure with Stiffening Characteristics
### The Relationship Between Dynamic Characteristics and Buckling  
As previously described, dynamic characteristics of a system reflect the motion trend of an object and are related to its stiffness. An intuitive explanation for the phenomenon of buckling is that when force is applied to a structure, it generates stress on the structure, which changes the stiffness of the structure. As the load increases, the stiffness of the structure may continue to decrease. When the force reaches the critical state where the structure buckles, applying a small disturbance to the structure will cause it to fail. At this time, analyzing dynamic characteristics will result in that one natural frequency approximates to zero [3].  
Gradually increase the load from 1 N to 120 N and calculate the dynamic characteristics of the panel with ribs and frames. Figure 9 shows the first-order and second-order eigenvalues in the dynamic equation of the panel with ribs and frames, as well as their difference.  
Figure 8: The buckling modes of the panel with ribs and frames: (**a**) 1 st-order mode; (**b**) 2 nd-order mode; (**c**) 3 rd-order mode.  
Figure 9: Load–frequency curve.  
From Figure 9, it is seen that both the first-order and second-order frequencies of the panel with ribs and frames decrease as the load increases. When the load is close to the first-order eigenvalue obtained in the eigenvalue buckling analysis, the first-order natural frequency decreases to zero. This occurs until a turning point appears when the force is about 90 N. The difference between the first-order and second-order frequencies is constantly increasing.
## 1 Introduction
Consequently, the benefit of applying this method to fault feature extraction for rolling bearings has been well proven. [PERSON] et al. proposed a semi-automated bearing diagnostic procedure, which combined order tracking with linear prediction, minimum entropy deconvolution, spectral kurtosis, and envelope analysis [20; 21]. However, this method mainly dealt with traditional REBs at slightly fluctuant operating speed, without considering two different rotating speeds and sharp changes in speeds. [PERSON] et al. [22] combined SK and order tracking with envelope demodulation to effectively diagnose the faults in rolling bearings of rotating machinery working at variable speeds. However, this method is mainly suitable for equipment with a single rotor and can be utilized when the vibration signal is directly measured at the housing of the faulty bearing. At present, the majority of the researches on extracting the fault features of REBs focus on traditional REBs used with a single rotor and fixed housing. However, it remains difficult to extract the fault features of intershaft bearings using these methods. This study considers inner and outer rings with variable rotation speeds. It proposes an order tracking method in the rotating-speed difference domain, which differs from traditional order tracking. Meanwhile, prewhitening based on linear prediction and SK is also adopted to extract the weak fault features of the intershaft bearing. Based on the above approaches for extracting the weak fault features of intershaft bearings, a scheme composed of linear prediction, SK, and order tracking in the rotating-speed difference domain is proposed. The fault diagnosis of intershaft bearings can effectively be performed using this proposed scheme when equipment with dual rotors operates at variable speeds. The structure of this paper is as follows. Section 2 explains the computing method for calculating the FCF of intershaft bearings and the prewhitening processing method based on linear prediction. The theoretical bases of SK and order tracking are also presented. Section 3 describes the proposed scheme, which adopts linear prediction, SK, and order tracking in the rotating-speed difference domain to extract the weak fault features of the intershaft bearings. Moreover, the application of this scheme is discussed in this section. The simulated and experimental signals of intershaft bearing faults are analyzed in Sections 4 and 5, respectively, confirming the effectiveness and value of the methods discussed in this paper. Section 6 presents the conclusions of the study.
## 5 Numerical Experiments
### Experimental Settings  
We conducted case studies in this study using varying price sensitivity and costs. The lead time \(l=1\), expiration period \(M=3\), maximum order quantity \(a_{\max}=10\), and the regular price of the product \(P=10\) remain consistent across all cases. Additionally, based on study [24], the demand follows a gamma distribution with a mean \(\mu=4\) and a coefficient of variation \(CV=0.50\). The holding cost \(c_{h}=1\), ordering cost \(c_{o}=3\), lost sales penalty \(c_{l}=5\), and perishable cost \(c_{p}=7\) were also set. Price setting is determined by selecting one of the discount rates \(P=\{0,30,50\}\). For example, selecting 30 changes the product price from 10 to 7. Price sensitivity is defined for each remaining shelf life, and since the expiration period is three days, it is represented as a three-dimensional vector. Table 3 presents the baseline parameter settings.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(l\) & 1 \\ \(m\) & 3 \\ \(a^{max}\) & 10 \\ \(p_{list}\) & 10 \\ \(\mu\) & 4 \\ \(CV\) & 0.50 \\ \(c^{h}\) & 1 \\ \(c^{o}\) & 3 \\ \(c^{l}\) & 5 \\ \(c^{p}\) & 7 \\ \(P\) & \(\{0,30,50\}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Basic parameter settings.  
Table 4 shows the parameter settings with varying price sensitivity and costs. Case 1 is the baseline case, and Cases 2 to 4 vary the price sensitivity. Cases 5 to 7 vary the costs.  
In this experiment, 365 steps constitute one episode, and performance is evaluated based on the total profit of an episode. The total profit is defined as the total revenue minus the total cost. Additionally, the optimality gap \(\delta\) is calculated using the results from DP as the optimal solution and the values obtained solely from PPO. It is computed using Equation (18).  
\[\delta=\frac{V_{\text{PPO}}-V_{\text{DP}}}{V_{\text{DP}}} \tag{18}\]  
The parameter settings for PPO used in this experiment are shown in Table 5. Since PPO demonstrates stable performance with minimal impact from parameter settings, all experiments were conducted using the same parameter configuration.
# A Green Computing Business Aggregation Strategy for Low Earth Orbit Satellite Networks
## 5 Simulation Analysis  
### Simulation Parameter Design  
Using the Systems Tool Kit (STK), a three-layer network architecture is modeled, with control satellites at 2000 km altitude and 18 orbital planes of business satellites (40 per plane) distributed between 500 and 1500 km in altitude. The power range of the entire functional module of the business satellite for sending intersatellite signals is 10 W-50 W [10], the power range of the entire functional module for receiving signals is 5 W-25 W [10], and the power range of the entire functional module for computing functions is 60 W-415 W [11]. The clock frequency of the satellite CPU is 2 \(\times\) 10\({}^{10}\) cycle/s [12]. The number of businesses ranges from 1000 to 9000, and these businesses are randomly distributed across different satellites [13]. The parameters of LSNs are shown in Table 1.  
The Double DQN strategy parameters are listed in Table 2. The learning rate was set to 0.001, ensuring a stable parameter update step size, with an epsilon decay rate of 0.995 to balance exploration and exploitation phases. The discount factor \(\gamma\) was set to 0.99 to emphasize the importance of long-term rewards, and the training involved 500 episodes.
# Experimental Study of Coaxial Jets Mixing Enhancement Using Synthetic Jets
## 3 Results and Discussion
### The Parameter Distribution of Coaxial Jets
#### 3.1.2 Streamwise Velocity and Radial Velocity  
Because of the axial symmetry of coaxial jets, only parameters distribution on the positive Y-axis are shown in Figures 4 and 5. The radial distribution of the dimensionless time averaged streamwise velocity at different axial positions is shown in Figure 4a-c. At x/D\({}_{\rm{oa}}\) = 0.12, the inner and outer shear layers were thin, and the velocity difference in layers was great, so the velocity gradient was large, and the slope of the curve in the region of two shear layers was steep. However, the velocity of the annular jet between two shear layers was relatively stable, so the curve there was straight. With the development of the mainstream, the shear layers became thicker and the straight section became smaller. Under the influence of actuators, the length of the straight section rapidly shortened and disappeared at x/D\({}_{\rm{oa}}\) = 2. A similar change occurred at these three positions after the actuators are turned on: In the radial direction, the streamwise velocity rose in the inner shear layer, then decreased on the inside of the outer shear layer and increased on the outside. These trends and the velocity distribution in Figure 3 were mutually evidenced, showing the characteristics of the flow field in the inner and outer shear layers being affected by synthetic jets.  
The radial distribution of the dimensionless time averaged radial velocity at different axial positions is shown in Figure 4d-f. The radial velocity represented the radial penetration capability of the jet. Although the synthetic jet moved toward the center of the coaxial jets, it affected the mainstream to spread radially outward. At the positions corresponding to the inner and outer shear layers, the distribution of radial velocity exhibit a \"peak-valley\" shape, consistent with the counter-rotating vortex pairs. As the streamwise vortices dissipate along the axial direction, the radial velocity value continuously accelerated. Therefore, the \"peak-valley\" shape moved outward in the radial direction, and the shape amplitude became larger. After the actuators were turned on, the synthetic jets increased the streamwise vortices intensity, so the \"peak-valley\" shape amplitude at each selected position was significantly increased, especially near the outer shear layer. This means that the mainstream radial expansion capability was increased greatly by the synthetic jet, and the internal radial mixing was enhanced.
## 5 Conclusions  
In the current experimental study, an effort has been made to determine the feasibility of FSW for dissimilar materials: AA 6061-T6 and Cu B370. The findings of the experiments are follows.  
1. Dissimilar materials, AA 6061-T6 and Cu B370, were successfully joined using FSW at various tool rotation speeds, weld speeds, and probe offsets towards the softer material, Al, side.
2. An excellent weld joint and surface was obtained at a probe offset of 2.0 mm towards the Al side and with the configuration of keeping the copper plate at the AS and the aluminum plate at the RS.
3. From the F-test of ANOVA and from scatter diagrams, it can be concluded that the formulated statistical (mathematical) model is suitable enough to predict the output responses at a 95% confidence level.
4. From the experimental investigations, it was observed that the maximum tensile properties were obtained at a higher tool rotation speed by keeping the weld speed  
Figure 14: SEM morphology of fracture surface: process parameter = 1400 RPM, 65 mm/min, and probe offset = 1.0 mm.  
Figure 13: SEM morphology of fracture surface: process parameters = 1400 RPM, 65 mm/min, and probe offset = 2.0 mm.  
constant. A similar behavior was observed at a higher weld speed by keeping the tool rotation speed constant.
* From SEM morphologies of the fracture surfaces, it can be noted that when the probe offset is 2.0 mm, the failure mode is ductile in nature and the tensile properties are at their maximum. However, the tensile strength decreased as the probe offset became less. For a probe offset of 1.0 mm, the failure mode was found to be brittle in nature.  
[PERSON] has done the experimental part and all analysis part. She has drafted the initial paper. [PERSON] is in continuous correspondence with the first author during the research work. Second author reviewed the paper and with the consultation of first and third author, final format of the manuscript is prepared for submission. [PERSON] has done the final editing and put his critical comment in the improvement of the paper. [PERSON] done final editing of paper with the third author. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The authors would like to extend their sincere gratitude to the Mechanical Engineering Department, and Friction Stir Welding Lab of IIT Kharagpur, India, for allowing us to conduct FSW experiments in their laboratory.  
The authors declare no conflict of interest.
# DPS Clustering: New Results
## 4 Results: Third Stage of DPS
### Second Part: Final Selection of \(D\)-Clusters
#### 4.3.1 Boolean Variant  
Expert E decides whether \(d\)-cluster \(C\) is included in the final result \(\text{DPS}_{f}(X)\) based on comparison of \(r(C)\) and \(e(C)\) with their proximity and distance thresholds \(r_{\text{E}}\) and \(e_{\text{E}}\): \(C\in\text{DPS}_{f}(X)\Leftrightarrow r(C)\leq r_{\text{E}}\) and \(e(C)\geq e_{\text{E}}\).  
Figure 6: Complete block-diagram of the DPS-algorithm.  
Expert E considers the parameter \(r\) of the DPS-algorithm to be very small (infinitesimal), much less than \(r_{\text{E}}\), which, in turn, according to E, is much less than \(e_{\text{E}}\):  
\[r\leq r_{\text{E}}\leq e_{\text{E}}.\]  
The threshold \(r_{\text{E}}\), like the radius \(r\) in the DPS-algorithm, is built using the Kolmogorov averaging of non-trivial distances of the FMS \(X\)[5]  
\[r_{\text{E}}=\Bigg{(}\frac{\sum_{x\
eq y\in X}d(x,y)}{|X|(|X|-1)}\Bigg{)}^{1/ \!q},q=q(r_{\text{E}})\]  
For the parameter \(r\), numerous applications of DPS-series algorithms have established that the choice of \(q(r)\in[-3,-2]\) can be considered optimal. The studies carried out within the framework of present paper show that \(q(r_{\text{E}})\in[-2.5,-1,5]\). The intersection of the areas of parameters \(r\), \(r_{\text{E}}\) is explained both by the fuzzy perception of proximity by the expert and by the diversity in the arrangement of an arbitrary FMS.  
The threshold \(e_{\text{E}}\) is obtained from \(r_{\text{E}}\) by formalizing the expert judgment \"\(r_{\text{E}}\ll e_{\text{E}}\)\" using fuzzy comparison. For the comparison given in Example 1, this would be the inequality \(e_{\text{E}}/r_{\text{E}}\geq 5/3\).  
**Example 3**.: _Array \(X\) in Figure 7a has already been seen above: it was a testing ground for DPS, DBSCAN, and OPTICS algorithms. Figure 7b-d show the complete DPS-scenario with parameters \(\beta=-0.25\), \(q(r)=-2.7\), \(q(r_{\text{E}})=-2.3\), \(e_{\text{E}}/r_{\text{E}}=5/3\)._  
_The result of cutting at the first stage (Figure 7b) was divided into eight \(r\)-connectivity components at the second stage (Figure 7c). Two components (blue and light brown) and two more in a green combination passed independently to the third stage (Figure 7d). The remaining four brown components did not pass the third stage._  
**Example 4**.: _In the conditions, notation and parameters of Example 1 Figure 8a-c show the original array and two stages of the SDPS algorithm on \(it\). Figure 8d-f show the complete scenario for his second iteration of SDPS\({}^{2}\)._  
Figure 7: The complete scheme of the SDPS algorithm: (**a**)—initial array; (**b**)—the first stage; (**c**)—the second stage; (**d**)—the third stage.  
Comparison of Figure 8c_f_ _serves as clear evidence of the work done in the paper: the full version of the second iteration of SDPS\({}^{2}\) with the same parameters performs better than the incomplete zero version (the original SDPS algorithm) that existed before this article._