esa-sceva/satcom-chunk-collection huggingface dataset:
# GUI for Analysis of Parameters, Accurate Design and Optimization of Microstrip Filters
## 4 Computer Algorithms
### Calculation of \(W_{Lim}\) and \(W_{Clim}\)  
In this section, Algorithm 3, which finds the boundary values for \(W_{C}\) and \(W_{L}\), discussed in Section 3.1, is developed. It is important to determine these values to use physically valid line widths in the filter design. MicroStripOptim uses this algorithm in all analyses that take into account the maximum and minimum limits of microstrip widths. This enables the visualization of value ranges such as filter lengths, allowable maximum and minimum attenuations, and so on.  
What Algorithm 3 does to find these boundary values is first to determine the value of \(W_{Llim}\) as follows: Initially, a high value is assigned to \(W_{Lhigh}\) and a small value to \(W_{Llow}\), with a high value for \(W_{C}\). While the difference \(\Delta W_{L}\) is greater than the programmed precision, an iterative loop is executed where the values of \(W_{Lhigh}\) and \(W_{Llow}\) are adjusted based on an \"if\" statement. If any of the lengths are complex, \(W_{Lhigh}\) is reduced; otherwise, \(W_{Llow}\) is increased. Upon exiting the while loop, \(W_{Llim}\) is assigned the value of \(W_{Llow}\).  
Next, the algorithm finds \(W_{Clim}\) by proposing a high value for \(W_{Chigh}\) and setting \(W_{Cllow}=W_{0}\), while assuming a small \(W_{L}\). While the difference \(\Delta W_{C}\) is greater than a desired precision, a repetitive loop is executed where an \"if\" statement adjusts \(W_{Cllow}\) or \(W_{C}\) depending on whether the resulting lengths produce complex values. At the end of the loop, \(W_{Clim}\) is assigned the value of \(W_{C}\).  
```
\(W_{0}\), \(f_{c}\), \(n\), \(k\), \(\varepsilon_{r}\) \(W_{Llim}\) and \(W_{Clim}\) \(precision\gets 1\times 10^{-5}\) \(WL_{high}\gets W_{0}\); \(WL_{low}\gets 0.01\)\(\triangleright\) Compute \(W_{Llim}\) \(\Delta W_{L}\gets WL_{high}-WL_{low}\) while(\(\Delta W_{L}>precision\))do \(W_{L}=WL_{low}+\Delta W_{L}/2\); \(W_{C}=10W_{0}\)  Compute total length \(TL\), for current \(W_{L}\) and \(W_{C}\) if\(TL\) is complex then \(WL_{high}\gets W_{L}\) else \(WL_{low}\gets W_{L}\) endif \(\Delta W_{L}\gets WL_{high}-WL_{low}\) endwhile \(W_{Llim}\gets WL_{low}\) \(WC_{high}\gets 20W_{0}\); \(WC_{low}\gets W_{0}\)\(\triangleright\) Compute \(W_{Clim}\) \(\Delta W_{C}\gets WC_{high}-WC_{low}\) while(\(\Delta W_{C}>precision\))do \(W_{C}=WC_{low}+\Delta W_{C}/2\); \(W_{L}=WL_{lim}/2\)  Compute total length \(TL\), for current \(W_{L}\) and \(W_{C}\) if\(TL\) is complex then \(WC_{low}\gets W_{C}\) else \(WC_{high}\gets W_{C}\) endif \(\Delta W_{C}\leftarrow WC_{high}-WC_{low}\) endwhile \(W_{Clim}\gets WC_{high}\)
```  
**Algorithm 3** Algorithm for computing \(W_{Llim}\) and \(W_{Clim}\)
# Analysis of Wafer Warpage in Diamond Wire Saw Slicing Sapphire Crystal
## 3 Finite Element Modeling
### Modeling and Solution  
Based on ABAQUS software (v.5.4), a finite element model of diamond multi-wire sawing sapphire crystal is established, as shown in Figure 2. In the finite element model, the dimensions of the workpiece are set to 10 mm \(\times\) 10 mm \(\times\) 6.9 mm, and the bottom surface of the workpiece is completely fixed. The diamond wire moves along the \(x\)-axis. At the same time, to achieve the feed of the workpiece relative to the diamond wire in the \(y\)-direction during the cutting process, the feed of the diamond wire relative to the workpiece in the \(y\)-direction should be set in the simulation model. This is achieved by applying cutting heat flux to the kerr in the simulation model instead of the actual diamond wire used in machining. The cutting process of forming five slices from six kerfs was simulated, with a kerr width of 0.3 mm and a slice thickness of 0.5 mm. Using the sequential coupling method for calculation, the temperature field generated during the sawing process was first calculated, and the thermal deformation caused by the temperature field was first calculated. In the finite element model, an eight-node linear heat transfer hexahedral element DC3D8 and an eight-node linear hexahedral element C3D8I are used. The total number of units is 41,280, and grid independence verification has been conducted.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material Parameters** & **Value** & **Material Parameters** & **Value** \\ \hline Density/g\(\cdot\)cm\({}^{-3}\) & 3.95 &
\begin{tabular}{c} Thermal \\ conductivity/W/(cm\(\cdot\)K) \\ \end{tabular} & 132.5 \\ Hardness (Moh’s)/GPa & 9 & Fracture toughness/MPa\(\cdot\)m\({}^{1/2}\) & 2.02 \\ Volume modulus/GPa & 250 & Young modulus/GPa & 320–340 \\ Poisson’s ratio & 0.28 & Thermal expansion & 5.8 \\  & & coefficient/(\(\times\)10\({}^{-6}\cdot\)K\({}^{-1}\)) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material parameters of the sapphire crystal.  
Figure 2: Multi-wire sawing sapphire model: (**a**) 3D geometric model, (**b**) finite element model and mesh division.  
When calculating the temperature field, all surfaces exposed to the air are set as natural convection boundary conditions, with a natural convection heat transfer coefficient of 5 W/m\({}^{2.\circ}\)C [7], and the value of the forced convective heat transfer coefficient between the workpiece and the coolant is 3 \(\times\) 10\({}^{4}\) W/m\({}^{2.\circ}\)C [18]. The ambient temperature is set to 25 \({}^{\circ}\)C. The simulation of the sawing process is divided into multiple steps, with each step using the birth and death element method to remove a certain depth of material. The killed element is considered to be cut off, and it is no longer subjected to a sawing action and is deleted in space, allowing the cutting heat flow to act on the new cutting area, thus simulating the entire sawing process. Figure 3 shows the finite element simulation flowchart.
# Stability Analysis of Buck Converter Based on Passivity-Based Stability Criterion
## 1 Introduction
What is more, this criterion could also be suitable for the bidirectional energy flow system, such as the energy storage system, electric vehicle charging and so on. If the subsystems of the DC microgrid are designed to be passive, then the whole DC microgrid system will be passive. In addition, the stability of the system will be ensured. In this paper, a Buck-CPL subsystem of DC microgrids is chosen as an example for the stability analysis, which is commonly used in DC microgrids. Further, on the basis of the classical double-loop control scheme, an improved feedback control method is proposed to improve the stability of the system. The structure of this paper is as follows. The proposed PBSC method is introduced in Section 2. In Section 3, the detailed mathematical model of the Buck converter is established, and its passivity is analyzed. In Section 4, an improved control strategy is proposed to meet the requirements of PBSC and improve the stability of the system. In Section 5, the simulation is carried out in MATLAB/Simulink to verify the effectiveness of the proposed improved control strategy. Finally, Section 6 summarizes the main contributions of this paper.
###### Abstract  
The aim of this study was to examine the effects of sandstone ring specimens with different inner diameters on dynamic compression mechanical characteristics after dry and wet circulation. To carry out our study, we subjected a sandstone ring specimen with a 50 mm outer diameter and a 0-25 mm inner diameter to 10 cycles of dry and wet circulation. Afterward, we recorded the specimen's basic physical parameters and used a split-Hopkinson pressure bar (SHPB) test device to perform an impact compression test. Following dry and wet circulation, our results showed that the mass loss rate increased and the volume expansion rates and density decreased with the increase in the inner diameter of the sandstone ring sample. Simultaneously, with the increase in the inner diameter of the specimen ring, the dynamic compressive strength of the specimen presented an exponential negative correlation, the dynamic elastic modulus presented a quadratic negative correlation, and the dynamic peak strain presented a quadratic positive correlation. Concurrently, the average particle size of the specimen decreased, and the degree of breakage increased with the increase in the sandstone sample's inner diameter. Regarding the energy analysis performed in this study, the sandstone ring sample's energy dissipation increased, and its kinetic performance evidently weakened with the increase in the ring sample's inner diameter. The results of this study have certain reference values for the construction and maintenance of natural cavity rock and underground hard rock roadways.  
rock dynamics; ring sample; dynamic compression; energy; SHPB (split-Hopkinson pressure bar) +
Footnote †: journal: Applied Sciences
# Reduction of Phase Shifters in Planar Phased Arrays Using Novel Random Subarray Techniques
## 2 Random Subarray Design Methodology
### Planar Array as Matrix of Integers  
As mentioned in [13], a linear array with subarrays can be represented as an array of integers (sequence), where each integer corresponds to the number of antenna elements in a given subarray \(K\) of size \(j\), and then the number of phase shifters \(PS_{m}\) or subarrays per row is given by  
\[PS_{m}=\sum_{j\in J\subset N}K_{j}, \tag{6}\]  
where \(m\in[1,2,\ldots,M-1,M]\) denotes the \(m\)-row in the array and \(J\) the subset of natural numbers that compose the different sizes of subarrays \(K_{j}\).  
Therefore, a matrix of \(PS_{m}\times M\) integers is used to represent the configuration of the planar array, where \(PS_{m}\) represents the number of subarrays per row \(m\) and \(M\) the number of rows, where each row can be visualized as a linear array of \(K_{j}\) number of subarrays, as shown in Figure 1. The total number of phase shifters used by the planar array is calculated by the sum of all subarrays in the array, such as  
\[PS_{total}=\sum_{m=1}^{M}PS_{m}. \tag{7}\]
# Multi-Scale Safety Helmet Detection Based on SAS-YOLOv3-Tiny
## 4 Experiments and Results Analysis
### Dataset and Evaluation Criteria
#### 4.1.2 Evaluation Criteria  
The quality of the detection effect usually needs a certain standard to evaluate, so the following evaluation criteria are introduced.  
(1) The formulas of the Precision and Recall are shown in Formula (10), and the formula of F1 is shown in Formula (11). F1 is the harmonic mean of Precision and Recall. In Formula (10), True Positives (TP, \(IOU\geq\) threshold) refers to the number of instances that are actually positive examples and are classified as positive examples by the classifier. False Positives (FP, \(IOU<\) threshold) refers to the number of instances that are actually negative examples but are classified as positive examples by the classifier. False Negatives (FN, undetected ground truth box) refers to the number of instances that are actually positive examples but are classified as negative examples by the classifier.  
\[\text{P}_{precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}\quad\text{R}_{ recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{10}\]  
\[\text{F1}=\frac{2\text{PR}}{\text{P}+\text{R}} \tag{11}\]  
(2) The formulas of Average Precision (AP) and Mean Average Precision (mAP) are shown in Formula (12).  
\[\text{AP}=\int_{0}^{1}\text{P}(\text{R})d\text{R}\quad\text{mAP}=\frac{\sum_{ i=1}^{\text{N}}\text{AP}_{i}}{\text{N}} \tag{12}\]In Formula (12), N represents the number of object categories. In general, the increase of the Recall is often accompanied by a decrease in Precision. To better balance the two, the P-R curve is introduced, and the area below it is the AP value of a specific category.
# Novel Real-Time Acoustic Power Estimation for Dynamic Thermoacoustic Control
## 2 Materials and Methods
### Instantaneous Power Estimation  
By measuring the pressure difference in the resonator and in the small tank, it will be possible to determine the acoustic power lost, \(\dot{E}_{load}\), by simply multiplying this difference by the flow rate through the valve.  
\[\dot{E}_{load}=P_{V}\cdot U=(P_{E}-P_{C})\cdot U, \tag{10}\]  
where \(P_{V}\) is the pressure in the valve.  
Using the electro-acoustic analogy to the electrical power equation would leave  
\[\dot{E}_{load  
The instantaneous power in any element of an alternating current circuit is expressed as \(p(t)=v(t)\cdot i(t)\), where \(v(t)\) and \(i(t)\) represent the voltage and current at each moment, respectively. If the source supplying the circuit produces a sinusoidal alternating current, both the voltage and current of each element will be represented by sinusoidal waves. These waves will exhibit a relative phase shift, determined by the specific characteristics of the element where the instantaneous power is being evaluated.  
The expression for instantaneous power, \(P_{elec}(t)\), as a function of the voltage and current of an element of an electrical circuit is defined as  
\[P_{elec}(t)=v(t)\cdot i(t)=2\cdot V\cdot I\cdot\cos(w\cdot t)\cos(w\cdot t- \varphi), \tag{15}\]  
where V and I represent the root mean square (RMS) voltage and current of that element. \(w\) represents the angular frequency and \(\varphi\) is the phase angle between the voltage and the current of the element.  
By applying the trigonometric identities for sine and cosine of a double angle, this expression can be reformulated, as follows:  
\[P_{elec}(t)=P\cdot(1+\cos(2\cdot w\cdot t))+Q\cdot sen(2\cdot w\cdot t), \tag{16}\]  
where P and Q are the active and reactive power, respectively, consumed by the passive element.  
The average value of this instantaneous power is the active power, P, the power that provides the energy that is converted into effective or useful work, either in the form of heat, light, or motion.  
\[\overline{P_{elec}(t)}=P, \tag{17}\]  
In this case, the instantaneous power to be measured and calculated is that of the resistive element of the circuit, where the voltage at the terminals of the resistor and the current flowing through it are in phase. Figure 5 shows a singular case of the voltage and current in a resistive element, as well as the instantaneous power.
## 2 Data Description
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Data Name**} & \multicolumn{4}{c}{**Description**} \\ \cline{2-6}  & **Satellite** & **Wavelength (nm)** & \
\begin{tabular}{c} **Spatial** \\ **Resolution (km\({}^{2}\))** \\ \end{tabular} & **Retrieval Algorithm** &
\begin{tabular}{c} **Observation Time** \\ **(UTC)** \\ \end{tabular} \\ \hline MODIS-DT & Terra & 550 & \(10\times 10\) & C6 & 01:20–03:25 \\  & Aqua & & & 03:35–05:40 \\  & Terra & & & 01:20–03:25 \\ MODIS-DB & Aqua & & & 03:35–05:40 \\  & Terra & & & 01:20–03:25 \\  & Aqua & & & 03:35–05:40 \\ MODIS-3 km & Terra & 550 & \(3\times 3\) & C6 & 01:20–03:25 \\  & Aqua & & & 03:35–05:40 \\ VIRS & Suomi-NPP & 550 & \(6\times 6\) & EDR & 03:15–05:40 \\ MISR & Terra & 550 & \(17.6\times 17.6\) & V22 & 01:30 \\ OMI & Aqua & 500 & \(13\times 24\) & Near-UV & 04:05–05:35 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Detail description of satellite AOD values used in this study. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Name** & **Instrument** & **Wavelength (nm)** & **Retrieval Algorithm** \\ \hline AERONET & \begin{tabular}{c} CIMEL Electronique CE318 multiband \\ sun photometer \\ POM-02 \\ Skyradiometer \\ UV-MFRSR \\ VIS-MFRSR \\ Brewer spectrophotometer \\ \end{tabular}  & \ \begin{tabular}{c} 340, 380, 440, 500, 675, \\ sun photometer \\ POM-02 \\ Skyradiometer \\ Multi-Filter Rotating Shadowband \\ Radiometers (MFRSR) \\ Brewer Spectrophotometer (MK-IV, No. \\ 148) \\ \end{tabular}  &
\begin{tabular}{c} [15] \\ \(\!\!of the Songwol-dong AWS station, which is the closest site (\(\sim\)2 km) to Yonsei University. We also used the amounts of PM\({}_{2.5}\), surface ozone, and nitrogen dioxide (NO\({}_{2}\)) measured at the Seodaemun-gu surface station, which is the closest air pollution monitoring site to Yonsei University (\(\sim\)1.5 km). Both ozone and NO\({}_{2}\) data were obtained from the AIRKOREA data archive ([[https://www.airkorea.or.kr/](https://www.airkorea.or.kr/)]([https://www.airkorea.or.kr/](https://www.airkorea.or.kr/)), final accessed on 1 January 2021) and the unit is parts per billion (ppb). The quality control and assurance process was confirmed by the AIRKOREA data archive system; therefore, the minimum quality of PM\({}_{2.5}\), ozone, and NO\({}_{2}\) was ensured guaranteed. The analysis in this study was only performed when the PM\({}_{2.5}\) values were available. The site information of Yonsei University was described in detail in [PERSON] et al. (2010) [26].
## 7 Simulations and Results
### Scenario 3: HEMS with ESS and PV
#### 7.3.1 Cost Using RTP and CPP  
By considering the RTP tariff, the GWO, GA, and the proposed technique HGWGA optimally scheduled the consumer load in the presence of an ESS and PV, and the electricity consumption costs were 322.65 cents, 315.61 cents, and 299.95 cents, respectively. In comparison with the unscheduled case, the optimal scheduling cost using the GWO, GA, and the proposed HGWGA was reduced by 38.91%, 40.25%, and 43.22%, respectively. Similarly, when the CPP signal was applied, the respective electricity consumption costs were 296.36 cents, 268.79 cents, and 255.04 cents, which indicates that the consumption cost relative to the unscheduled case was reduced by 56.37%, 60.42%, and 62.45%, respectively. The total electricity consumption cost for the unscheduled and scheduled cases for scenario 3 using the RTP and CPP schemes is shown in Figures 18 and 19, respectively.  
Figure 17: Per-hour load with an ESS and PV.  
Figure 18: Total cost with an ESS and PV using the RTP.
## 4 Congestion Control Based on the RED Algorithm
### Analysis of the Advantages and Disadvantages of RED
The combination of congestion recognition based on the ACA and congestion control based on the RED algorithm enables efficient congestion management. It rapidly identifies congestion and dynamically adjusts the packet drop rate based on the actual congestion level, ensuring network stability and reliability. This combined algorithm optimizes network performance, enhances transmission efficiency, and provides a superior user experience.
# Molecular Mechanisms of Topography Sensing by Osteoblasts: An Update
## 4 Influence of Substrate Chemistry versus Topography  
The composition of the environment is another important parameter influencing osteoblasts' fate. As osteoblasts are adherent cells, a desirable substrate to promote osteogenic proliferation, differentiation and bone production must be biocompatible and hydrophilic [62; 98]. Some of the processes used to produce topographies on surfaces can change surface chemistry and hamper osteogenicity. Therefore, some surface processing can be used to improve cell adhesion on hydrophobic scaffolds, such as rinsing the surface under N2 protection and storage in saline solution at pH 4-6 to reduce carbon contamination [87; 99]. Another technique that can produce hydrophilic surfaces with a controlled surface chemistry is anodization (see Appendix A).  
Furthermore, the wettability of topographically structured surfaces will influence the adsorption of extracellular matrix proteins such as fibronectin [40; 71]. [PERSON] et al. demonstrated that hydroxyapatite can adsorb more proteins and promote higher numbers of focal adhesions than titanium alloys or stainless steel [100]. The incorporation of magnesium in titanium surfaces performed by [PERSON] et al. also increased fibronectin adsorption, enhancing their biocompatibility [101]. [PERSON] et al. observed a synergistic effect of a microscale surface and strontium doping on topography, inducing osteogenicicity, better together than individually [84,101]. [PERSON] et al. showed that fibronectin was adsorbed selectively on grooves/ridges boundaries, which may had influenced the preferential location of osteoblasts' focal adhesions [41]. This observation suggests that adsorbing proteins cannot be totally effective depending on surface configuration and can overlap topography influence. Recently, cell contact guidance along micrometric grooves on titanium was abrogated by a plasma polymerized allyamine coating while it was maintained after methyl- or carboxyl-plasma polymerized coatings, type I collagen coating or immobilization of the integrin adhesion peptide sequence Arg-Gly-Asp (RGD) [102]. This was the first demonstration of the possibility of overcoming a strong topographical signal by changing the surface chemistry and further studies must be performed to determine the role of physicochemical surface properties on this cell behavior and the biological mechanisms involved. Finally, all these findings create clues illustrating the importance of investigating the separate and synergistic influences of surface chemistry and topography in order to define the best approach to efficiently functionalize artificial biomaterials' surfaces.
# HFDF-EffNetV2: A Lightweight, Noise-Robust Model for Fault Diagnosis in Rolling Bearings
## 4 Discussion
### Case 1: CWRU Bearing Dataset
#### 4.2.3 Model Comparison Experiments and Analysis Under Noise Interference  
According to Equation (13), GWN with varying SNRs was added to the test set signals to evaluate the robustness of the model to noise. The experimental results are shown in Figure 9.  
After noise was added, the proposed model achieved an average accuracy of 98.19%, which is 18.39%, 11.23%, 5.07%, 2.38%, 2.37%, and 1.51% higher than the six comparison models. Furthermore, the proposed model performed optimally under the same noise intensity compared to the other models. As the noise intensity increased, the accuracy decreased by only 3.72%, which is 1.05% less than the decrease observed in the next-best model, EfficientNetV2-S. At 2 dB, the accuracies of SqueezeNet and MobileNetV3 dropped dramatically, to 61.77% and 76.14%, respectively; these values were 34.11% and 19.74% lower than that of the proposed model, showing a significant gap. ResNet-18, RepViT, and MPNet performed well at 6 dB and 8 dB but poorly at 2 dB, with their accuracies lower than  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Accuracy/\%** & **P/\%** & **R/\%** & **F1/\%** & **Parameters/M** & **Time/ms** \\ \hline EfficientNetV2-S & 100 \(\pm\) 0.00 & 100 \(\pm\) 0.00 & 100 \(\pm\) 0.00 & 100 \(\pm\) 0.00 & 20.10 & 28.73 \(\pm\) 0.84 \\ ResNet-18 & 99.71 \(\pm\) 0.00 & 99.71 \(\pm\) 0.00 & 99.71 \(\pm\) 0.00 & 99.71 \(\pm\) 0.00 & 11.18 & 17.53 \(\pm\) 0.32 \\ RepViT & 99.83 \(\pm\) 0.14 & 99.83 \(\pm\) 0.14 & 99.83 \(\pm\) 0.14 & 99.83 \(\pm\) 0.14 & 4.72 & 22.69 \(\pm\) 0.39 \\ SqueezeNet & 99.23 \(\pm\) 0.12 & 99.23 \(\pm\) 0.11 & 99.23 \(\pm\) 0.12 & 99.23 \(\pm\) 0.11 & 0.73 & 24.40 \(\pm\) 0.43 \\ MPNet & 99.63 \(\pm\) 0.07 & 99.63 \(\pm\) 0.07 & 99.63 \(\pm\) 0.07 & 99.63 \(\pm\) 0.07 & 2.50 & 21.45 \(\pm\) 0.58 \\ MobileNetV3 & 98.40 \(\pm\) 0.25 & 98.40 \(\pm\) 0.24 & 98.40 \(\pm\) 0.24 & 98.40 \(\pm\) 0.24 & 3.91 & 19.34 \(\pm\) 0.31 \\ HFDF-EffNetV2 (Ours) & 100 \(\pm\) 0.00 & 100 \(\pm\) 0.00 & 100 \(\pm\) 0.00 & 100 \(\pm\) 0.00 & 1.85 & 18.67 \(\pm\) 0.31 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of different models’ diagnostic performance in Case 1.  
Figure 9: Comparison of experimental results for different models under different SNRs in case 1.  
that of the proposed model by 16.02%, 6.05%, and 4.51%, respectively. EfficientNetV2-S, which had the greatest number of parameters, performs=ed well, though its accuracy was slightly lower than that of the proposed model, by 2.25%, at 2 dB. The results show that there is a relationship between the model's noise resistance and its parameter count. More importantly, optimizing the model architecture to enhance its feature extraction capabilities can significantly improve the robustness of the model under noise interference.