esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Different Welding Methods on Flip-Chip LED (FC-LED) Filament Properties
## 3 Results and Discussion  
### Shearing Force Scatter Diagram and Microstructure of Fracture Surface  
Figure 2 shows the chip shearing force of direct welding (DW) and vacuum furnace welding (VFW). It can be seen that the shearing force of the chip using the two welding methods is higher than the standard shearing force of 200 gf; all of the shearing force met the requirements of the chip shearing force for FC-LED filaments. According to the average shearing force of the chip, the DW group was 306.08 gf and the VFW group was 356.46 gf. The VFW group was 16.5% higher than the DW group [18,19].  
Figure 3 shows the microstructure of the solder joint after the shear failure test. As can be seen from Figure 3, the microstructure of the solder joint using direct welding is very rough, and the cracks and voids are distributed; the solder joint using vacuum furnace welding was smooth. Combined with the shearing force of the two chip groups in Figure 2, it can be seen that the cracks and voids in the solder joint have a negative effect on the shearing force.  
In the case of the chip welded using the vacuum furnace, and because the temperature follows curve heating during the welding process, the flux in the solder reaches the volatile temperature and then volatilizes, after which the solder reaches the melting point and then melts. Compared with the instant melting of the solder using direct welding, vacuum furnace welding is more favorable for fluxing agent volatilization, and it can effectively avoid solder joint porosity caused by fluxing agent volatilization over time. Additionally, the welding environment of the vacuum furnace is a vacuum, which effectively prevents the oxidation of the elements in the solder at high temperature and avoids the production of cracks at the solder joint. Therefore, the shearing force of the VFW group is higher than that of the DW group [20, 21, 22].  
Figure 1: (**a**) Flip-chip light-emitting diode (FC-LED) chip physical diagram, (**b**) temperature curve.
## 2 Methodology  
In this section, we present the mechanical design of the track-following servo subsystem. The subsystem comprises a moving crane system, a moving trolley system, and multiple motors that enable the track-following servo in both the \(X\) and \(Y\) directions, as illustrated in Figure 2. The crane is driven by four motors, and the trolley is driven by two motors. The motors power the steel wheels of the crane and trolley to move on the rail. The servo motors transmit power to the system, which is then transmitted to the low-speed shaft end of the reducer via the coupling. The reducer's low-speed shaft end outputs to the driving wheel shaft through another coupling, and the driving wheels propel the crane and trolley to follow the CLRV with high precision.  
Then, based on the mechanical design of the subsystem, suitable equipment is selected, and a simulation model of the track-following servo subsystem is established. In this paper, the servo motors used are PMSMs. The track-following servo subsystem comprises a position servo closed-loop and closed-loop control of the motor speed and current; the crane control model is illustrated in Figure 3. The position control loop is situated on the outer layer of the double closed-loop, which includes the speed and current loops of the servo motor. The position control loop receives a reference signal and subsystem state variables and calculates the expected velocity increment of the crane or trolley in a given unit of time. This value is then passed to the motor's speed loop controller. The speed loop controller calculates the expected current output by comparing the expected speed with the actual speed. The motor's current loop controller then calculates the expected voltage input based on the expected current and the actual current. Together, these three loops form the track-following servo subsystem.  
To achieve high-precision control of the crane and trolley, the PMSM vector control method based on SVPWM is adopted for fast response in speed control. The PI control method, with its advantages of being a simple algorithm, having good stability, and having high reliability in the motor speed control system, is widely used for controlling motor speed and current. Therefore, the PI method is applied to the PMSM vector control system, as depicted in Figure 3.  
The performance of the outermost position servo loop plays a crucial role in the track-following servo subsystem's tracking performance. However, the position loop's control is limited by the electromechanical system's kinematics and dynamics. To overcome this multi-parameter constraint, the present paper proposes utilizing the EMPC controller within the position servo closed-loop.  
Figure 2: Schematic diagram of the subsystem movement.  
This section mainly describes the establishment of the mathematical model for the three-loop control system, including the mathematical model and model parameters of the PMSM, as well as the mathematical model of the position loop.
# Development and Characterization of High-Fiber, Gluten-Free Pasta for Celiac Disease Patients
## 3 Results and Discussion
### Chemical Composition of the Pasta Treatments
#### 3.1.6 Carbohydrate Content (NFE)  
The percentages of carbohydrate content of the control sample and pasta treatments were 62.38, 59.68, 57.93, 55.90, 49.56, and 46.10 for T1, T2, T3, T4, T5, and T6, respectively, as compared to the control group (60.84). The percentage of carbohydrate content in the gluten-free pasta treatments varied significantly (\(p<0.05\)) from each other. It is shown that T1 has the highest carbohydrate content compared to all other treatments since it contains 50% rice flour, 50% corn starch, and no flaxseed or lupin flour. Table 7 illustrates that the carbohydrate content was also high in the control sample (100% wheat flour), since wheat flour contains 78.10% carbohydrates [31]. It is shown that the percentage of carbohydrates in gluten-free pasta treatments decreased as the percentage of flaxseed and lupin flour incorporated into the samples increased. The results may be attributed to flaxseed's low carbohydrates (sugars and starch) [32] as well as lupin flour, which contains negligible amounts of sugar and starch [28].
## 3 Results
### Field Trials  
To ensure the representativeness of the data analysis, the acceleration response curve recorded by the acceleration sensor during the test was extracted specifically from the 20 s mark to the 40 s mark. This time interval was chosen as it is deemed more representative for data analysis. The robust data acquisition and analysis system focused on analyzing the acceleration response curve in the direction of the clamping mechanism during the vibration process. For visual reference, please refer to Figure 20, and for detailed numerical data, please consult Table 9.  
Based on the data presented in Figure 20 and Table 9, it is evident that the side branch acceleration increases as the excitation frequency (represented by the angle of the throttle switch knob) increases under the same clamping position. However, once the excitation frequency surpasses the optimal value, the side branch acceleration starts to decrease with further increases in the excitation frequency. This observation aligns with the conclusion drawn from the previous [PERSON] rigid-flexible coupling analysis.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Clamping Height/mm** & **Excitation Frequency/Hz** & **Average Acceleration/mm/s\({}^{2}\)** \\ \hline  & 9.70 & 31.10 \\
150.00 & 12.70 & 54.10 \\  & 15.70 & 51.30 \\  & 18.70 & 50.10 \\  & 9.70 & 32.30 \\
180.00 & 12.70 & 60.20 \\  & 15.70 & 56.40 \\  & 18.70 & 44.60 \\  & 9.70 & 46.30 \\
210.00 & 12.70 &
## 3 Results
### Structural Stability  
In order to test whether FeRhCrSi and FePdCrSi compounds can be synthesized and form a stable phase experimentally, the formation energy \(E_{f}\) and cohesive energy \(E_{c}\) were calculated in this work. The calculated values of the \(E_{f}\) and \(E_{c}\) are listed in Table 2. Taking FeRhCrSi as an example, the formation energy can be expressed by  
\[E_{f}=E_{tot}-E_{Fe}^{bulk}-E_{Rh}^{bulk}-E_{Cr}^{bulk}-E_{Si}^{bulk} \tag{1}\]  
Here, \(E_{tot}\) is the total energy of FeRhCrSi, \(E_{Fe}^{bulk}\), \(E_{Rh}^{bulk}\), \(E_{Cr}^{bulk}\) and \(E_{Si}^{bulk}\) are the total energies for each Fe, Rh, Cr, and Si atoms in their bulk states [37]. The bulk states that adopted in our calculation of Fe and Cr are body centered cubic(b.c.c) structure, Pd and Rh are face centered cubic(f.c.c) structure, and Si is diamond cubic(d.c) structure. The cohesive energy is expressed by  
\[E_{c}=E_{tot}-E_{Fe}^{iso}-E_{Rh}^{iso}-E_{Cr}^{iso}-E_{Si}^{iso} \tag{2}\]  
Here, \(E_{Fe}^{iso}\), \(E_{Rh}^{iso}\), \(E_{Cr}^{iso}\) and \(E_{Si}^{iso}\) refer to the energies of each isolated atom [37]. It can be found that formation energies for these alloys are both negative values, which are \(-3.12\) eV for FeRhCrSi and \(-1.97\) eV for FePdCrSi, indicating that these two alloys are stable and cannot be decomposed easily. Furthermore, these alloys have great negative cohesive energies, which are \(-20.41\) eV for FeRhCrSi and \(-18.66\) eV for FePdCrSi, implying that they have good chemical stability in practice and are likely to be synthesized in the experiment.
## 2 Materials and Methods
### Test Schemes  
The experimental design of this study builds upon the existing literature on the carbonation curing of metakaolin-based geopolymer concrete. Carbonation-induced carbonate formation has been shown to refine the pore structure of metakaolin-based geopolymers by increasing the closed porosity and reducing the porosity ratio [10]. However, previous studies primarily implemented carbonation curing under vacuum pressurization conditions, with limited discussion on the temperature-humidity effects and impractical engineering applicability. This study, therefore, employs atmospheric-pressure carbonation curing in a controlled chamber to investigate its application in alkali-activated slag-solidified shield muck. Temperature (10-30 \({}^{\circ}\)C) and relative humidity (60-80%) were selected as key variables to assess their impacts on carbonation efficiency. As detailed in Table 2, the 24 h curing duration was designed to meet the critical early-age mechanical performance requirements in shield engineering, aligning with tailings matrix research protocols [5]. Laboratory observations further confirmed stabilized hydration and strength development trends within this timeframe.  
As shown in Figure 2, alkali-activated slag-solidified shield muck specimens were fabricated via a cast molding process. Raw shield muck and slag micro-powder were dry-mixed for 5 min in a planetary mixer according to the mix proportions specified in Table 3, followed by 5 min of wet mixing after adding the alkaline activator (120 \(\pm\) 5 rpm). The homogenized mixture was cast into 40 mm \(\times\) 40 mm \(\times\) 40 mm holds and subjected to vibratory compaction for 60 s. After demolding, the specimens were parafilm-sealed and pre-cured for 3 days under controlled conditions (20 \(\pm\) 1 \({}^{\circ}\)C, 95 \(\pm\) 2% RH). Carbonation-cured specimens were subsequently sealed and stored under standard curing conditions  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Materials** & **CaO** & **SiO\({}_{2}\)** & **Al\({}_{2}\)O\({}_{3}\)** & **Fe\({}_{2}\)O\({}_{3}\)** & **SO\({}_{3}\)** & **MgO** & **TiO\({}_{2}\)** & **K\({}_{2}\)O** & **Na\({}_{2}\)O** \\ \hline Shield & 13.84 & 48.18 & 14.10 & 3.90 & 0.01 & 3.42 & 0.65 & 2.55 & 0.45 \\ GGBFS & 41.32 & 30.64 & 15.04 & 0.41 & 2.68 & 6.24 & 2.31 & 0.40 & 0.41 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of raw materials (wt%).  
(20 \(\pm\) 1 \({}^{\circ}\)C, 95 \(\pm\) 2% RH) for 28 days. The reference group (Ref) specimens underwent standard curing throughout (20 \(\pm\) 2 \({}^{\circ}\)C, 95 \(\pm\) 2% RH).
## 2 A Voltage Ride-Through (VRT) Control Model for Energy Storage Systems
### Hybrid Optimization Algorithm Design
#### 3.3.2 Global Optimization Phase  
The Particle Swarm Optimization (PSO) algorithm is employed for global search. The state update formula for particle \(i\) at iteration \(t\) is as follows:  
\[\left\{\begin{array}{l}v_{i}^{(t+1)}=\omega\times v_{i}^{(t)}+c_{1}r_{1} \Big{(}p_{best,i}^{(t)}-\theta_{i}^{(t)}\Big{)}+c_{2}r_{2}\Big{(}g_{best}^{(t) }-\theta_{i}^{(t)}\Big{)}\\ \theta_{i}^{(t+1)}=\theta_{i}^{(t)}+v_{i}^{(t+1)}\end{array}\right. \tag{20}\]  
In the equations, \(\omega\): inertia weight; \(c_{1}\), \(c_{2}\): learning factors; \(r_{1}\), \(r_{2}\)\(\sim\)U(0, 1): random numbers; \(p_{best,i}\): historical best position of particle \(i\); \(g_{best}\): global best position.  
Out-of-bound parameters are handled via reflective boundary conditions:  
\[\theta_{i,j}^{(t+1)}=\begin{cases}2\cdot\theta_{\text{min},j}-\theta_{i,j}^{(t +1)},&\text{if }\theta_{i,j}^{(t+1)}<\theta_{\text{min},j}\\ 2\cdot\theta_{\text{max},j}-\theta_{i,j}^{(t+1)},&\text{if }\theta_{i,j}^{(t+1)}> \theta_{\text{max},j}\\ \theta_{i,j}^{(t+1)},&\text{otherwise}\end{cases} \tag{21}\]  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter Category** & **Parameter Name** \\ \hline Active Power Setpoint & \(p_{r}^{*}\) \\ Reactive Power Setpoint & \(Q_{r}^{*}\) \\ Reactive Power Support Coefficient & \(K_{1},K_{2}\) \\ Power Loop Parameters & \(K_{P_{-l}p},K_{i\_{l}p},K_{P_{-l}q},K_{i\_{l}q}\) \\ Current Loop Parameters & \(K_{i\_{l}},K_{i\_{l}p}\) \\ Grid-side Maximum Current & \(I_{max}\) \\ \hline \end{tabular}
\end{table}
Table 1: Key Parameters of the Energy Storage Converter.
# Automatic Detection of Multilevel Communities: Scalable, Selective and Resolution-Limit-Free +
###### Abstract
The innovation of our work includes: (1) we introduce a new scaling factor to enhance the scalability of the community fitness function; (2) we suggest a strict strategy to automatically select the outputs, as well as an explicit definition for the term \"plateau,\" and (3) we demonstrate our community fitness function is scalable so that it can overcome the resolution-limit problem. To summarize, our approach is scalable, selective, and resolution-limit-free, with clean and inartificial outputs. It performs well on both synthetic benchmark networks and real-world networks.
## 2 Atmospheric Oxygen Transmittance Effects at Tower Scale
### Upward Atmospheric Transmittance from Surface TOC to Sensor Level  
Assuming a Lambertian surface reflectance, the Top Of Atmosphere (TOA) radiance, \(L_{TOA}\), can be written as Equation (2):  
\[L_{TOA}=L_{0}+\frac{(\overline{E}\cdot\rho+\pi\cdot F)\cdot t^{\dagger}}{\pi \cdot(1-S\cdot\rho)} \tag{2}\]  
where \(L_{0}\) is the atmospheric path radiance, \(S\) is the spherical albedo, \(t^{\dagger}\) is the total upward atmospheric transmittance (diffuse and direct contributions) from surface TOC to TOA, \(\overline{E}\) is the solar irradiance reaching the surface, \(F\) is the emitted SIF (\(F\) hereafter for brevity) and \(\rho\) the Lambertian surface reflectance. Adjacency effects are not considered in Equation (2), and we are assuming an infinite uniform target with surface reflectance, \(\rho\).  
In case of short target-sensor distances, \(\sim\)10 m, it is possible to: (a) neglect the contribution of the atmospheric path radiance, \(L_{0}\), [40]; (b) consider that the total upward atmospheric transmittance is dominated by the direct transmittance component (\(t^{\dagger}_{dir}\)\(\sim\)5 \(t^{\dagger}_{dir}\)); and (c) assume that the contribution of the spherical albedo at TOC and at sensor level is equivalent. Therefore, the relationship between the at-sensor radiance, \(L\), and the up-welling canopy radiance from the surface, \(\overline{L}\), can be simplified as:  
\[L\simeq\left(\frac{\overline{E}}{\pi}\rho+F\right)\cdot t^{\dagger}_{dir}= \overline{L}\cdot t^{\dagger}_{dir} \tag{3}\]  
where the \(t^{\dagger}_{dir}\) is the direct upward transmittance from the surface to the sensor level for a given viewing geometry. For the O\({}_{2}\)-A absorption band, the direct upward transmittance is mostly dominated by O\({}_{2}\) concentration (see Figure 2). Hence, for the O\({}_{2}\)-A absorption band and a near target-sensor distance, it is possible to assume that \(t^{\dagger}_{dir}\)\(\sim\)\(t^{\dagger}_{\text{O}_{2}}\), \(t^{\dagger}_{\text{O}_{2}}\) being the oxygen transmittance.  
Figure 1: The percent (%) relative difference (RD) between the at–sensor (\(E\)) and at–surface (\(\overline{E}\)) measured total solar irradiance for two different SZAs (20\({}^{\circ}\)and 60\({}^{\circ}\)) for a range of target–sensor distances between 0 to 50 m. Panels (**a–d**); show the solar irradiance RD (%) corresponding to the O\({}_{2}\)–B and O\({}_{2}\)–A regions, respectively. The black dashed–line in the panels (**c,d**) indicate the maximum RD reached in the O\({}_{2}\)–B region to facilitate the comparison. Note the difference in the RD(%) scales in (**a,b**) versus (**c,d**).  
In the particular case of multi-angular acquisition systems, e.g., the FUSION tower-mounted system [43], O\({}_{2}\) transmittance correction becomes even more critical if the objective aims to integrate measurements over the footprint or to compare contributions from multiple acquisition geometries, since atmospheric corrections should account for the varying optical path with viewing angularity.
## 2 Research Status
### Research Status of Small Sample Issues in Fault Diagnosis
[PERSON] [26] introduced conditional constraints to the semi-supervised generative adversarial networks and optimized the loss function to enhance its guidance for the generator and discriminator, thereby improving the generative adversarial network. The generative model and semi-supervised learning ability of the model were utilized to solve the problem of insufficient data samples and sample labeling in fault diagnosis. [PERSON] [27] proposed a multi-module generative adversarial network augmented with an adaptive decoupling strategy. This strategy uses an adaptive learning method to update the initialized random noise of the generator, enabling it to obtain a better combination for generating samples. Additionally, a reconstruction module provides stronger constraints for the generator, which greatly improves the quality of the generated samples. Based on the above research, it can be seen that the solution utilizing generative adversarial networks can directly address the issues of small sample size and class imbalance in fault diagnosis from the input source layer, reducing the complexity of data sampling and processing procedures. It also avoids the complicated process of building specific diagnostic models for different diagnostic objects. Consequently, focusing on complex integrated equipment such as box-type substations, this project constructs a data derivation model based on generative adversarial networks, aiming to solve the problem of small sample size in fault diagnosis of a box-type substation. To address the challenge of training a high-performance fault diagnosis model with small samples, this paper proposes a fault diagnosis method for box-type substations based on an improved CTGAN and AlexNet network. In this method, the self-attention mechanism is added to the generator of CTGAN. The SA-CTGAN data derivation model is constructed, and the data are enriched and enhanced based on the original samples, particularly those with fewer samples. This, in turn, addresses the imbalance of health status data and fault status data categories, as well as the scarcity of fault data, all at the input source level. Finally, the expanded data are used as the input for the AlexNet fault diagnosis model to complete the fault diagnosis task of the box-type substation.
## 2 Mathematical Modelling of Investigated Power System  
In this article, an interconnected two-area power system, where every area comprising diverse generation-utilities--as highlighted in Figure 1--is studied. The parameters of the system are obtained from References [17; 18; 19] and are listed in Appendix A. The different parameters of the plant are furnished in Table 1[18]. The GRC for the hydro and thermal utilities as well as the GDB for the thermal plants in both control areas are addressed in the system model. Referring to Reference [4], the Fourier coefficients of the governor dead band (GDB) are taken as \(N_{1}=0.8\) and \(N_{2}=-0.2/\pi\). A suitable GRC model of 10%/minute for the thermal utility of Figure 1 is applied to both raising and falling rates. On behalf of the hydro utility, a 270%/minute for raising and 360%/minute for falling generation unit of the GRC model is established [18]. Assume for area-1 the constant quantity of \(PF_{Th}\), \(PF_{Hy}\) and \(PF_{G}\) are the shares of power generation (PGs) utilities from the sources mentionedabove. As a result, under regular operating states, there is no mismatch between generation and load. Consequently, the power generated from the thermal, hydro and gas power utilities are given below:  
\[P_{G1}^{Thermal}=PF_{Th1}P_{G1} \tag{1}\]  
\[P_{G1}^{Hydro}=PF_{Hy1}P_{G1} \tag{2}\]  
\[P_{G1}^{Gas}=PF_{G1}P_{G1} \tag{3}\]  
Figure 1: Two-area interconnected power plant.  
Under nominal operating conditions, the aggregated power generated, \(P_{G1}\) for area-1 is given by  
\[P_{G1}=P_{G1}^{Thermal}+P_{G1}^{Hydro}+P_{G1}^{Gas} \tag{4}\]  
\[P_{G1}=PF_{Th1}P_{G1}+PF_{Hy1}P_{G1}+PF_{G1}P_{G1} \tag{5}\]  
and  
\[PF_{Th1}+PF_{Hyd1}+PF_{G1}=1 \tag{6}\]  
for small load change in area-1, Equation (4) can be modeled as:  
\[\Delta P_{G1}=\Delta P_{G1}^{Thermal}+\Delta P_{G1}^{Hydro}+\Delta P_{G1}^{Gas} \tag{7}\]  
Similarly, Equations (1)-(7) can be modeled for area-2. From Figure 1, the input to the controller is their respective ACEs, and the output is the signal \(u_{i}\). Based on References [3,4], the area control errors (ACEs) of both areas can be formulated as:  
\[ACE_{1}=B_{1}\Delta f_{1}+\Delta P_{tie12} \tag{8}\]  
\[ACE_{2}=B_{2}\Delta f_{2}+\Delta P_{tie21} \tag{9}\]  
where \(\Delta P_{tie12}\) and \(\Delta P_{tie21}\) are the perturbation in tie-line power interchange in area-1 and area-2, respectively. \(\Delta f_{1}\) and \(\Delta f_{2}\) are the frequency deviation in area-1 and area-2.  
**3. Controller Structure**  
The design structure of the suggested controller is described in this section. Initially, a briefly review of the fractional order is presented, followed by the design steps of the fractional order intelligent PID (iPID) controller, and then the proposed fractional order fuzzy pre-compensated intelligent PID (FOFP-iPID) controller is described.