esa-sceva/satcom-chunk-collection huggingface dataset:
# GUI for Analysis of Parameters, Accurate Design and Optimization of Microstrip Filters
## 6 Results and Discussion
### Filter Analysis with MicroStripOptim
To illustrate, we provide an example of the figure depicting the plots for the constant \(TL\) versus maximum attenuation. Figure 15 depicts the plots specifically for the case of a constant \(TL\) value of 78 mm. The first subplot exhibits the \(W_{C}\)-\(W_{L}\) curve for the constant \(TL\) constraint. Subsequently, the second and third subplots display the plots of \(W_{C}\)-\(\max(L_{A})\) and \(W_{L}\)-\(\max(L_{A})\), respectively, showcasing the range of values for \(W_{L}\) or \(W_{C}\). Finally, a three-dimensional curve is depicted with \(W_{C}\), \(W_{L}\), and \(\max(L_{A})\) as the respective axes. The analysis of constant \(TL\) curves reveals the existence of a maximum point, representing the optimal configuration that minimizes or maximizes a specific parameter while  
Figure 6:0 Curve for constant \(TL=78\) mm. The curve for constant TL (**lower** part) is generated by the intersection of the total length surface with a constant TL surface (**upper** part). Figure 6:1 \(\max(L_{A})\) along the curve for constant \(TL=78\) mm. The \(W_{L}\) vs. \(W_{C}\) curve is the same as the one obtained in Figure 6:2 For each point on that curve, the filter is simulated, and the parameter \(\max(L_{A})\) is obtained. The following graphs show \(\max(L_{A})\) versus \(W_{C}\), versus \(W_{L}\), and a 3D curve with these same points. keeping the filter length constant. The three parameters that can be optimized are the minimal width of the filter, maximal attenuation, and maximal attenuation rate. The GUI also generates a table that provides the optimal values for \(W_{C}\) and \(W_{L}\) in each optimization scenario. Figure 16 displays this table, where the user can choose the desired optimization criterion. For instance, when selecting the point of maximum attenuation, the table indicates the values \(W_{C}=6.183\) mm and \(W_{L}=0.6\) mm, with \(\max(L_{A})=28.7529\) dB. The obtained values of \(W_{L}=0.6\) mm and \(W_{C}=6.183\) mm were introduced in the edit-boxes of the GUI in order to obtain the scattering plot and the data table of Figures 7 and 8. In the table of Figure 8 it can be seen that \(TL\) is, effectively, 78 mm and \(\max(L_{A})=28.75\) dB. These findings validate the effectiveness of MicroStripOptim in accurately determining the optimal filter parameters while adhering to the desired length constraint.
###### Abstract  
Nucleosome sequence composition is closely associated with gene expression. The nucleosome-depleted region (NDR) within the transcriptional regulatory region of _S. cerevisiae_ genes plays a crucial role in the regulation of gene expression. To investigate the effect of NDR length on transgene expression, we conducted an experiment to regulate exogenous gene expression by manipulating the NDR in the transcriptional regulatory region of _S. cerevisiae_ genes. We selected the \(\pm 1\) nucleosome and NDR sequences between them of six _S. cerevisiae_ genes as transcriptional regulatory elements to drive GFP gene expression. Our findings revealed that under the regulation of long NDR, both the mRNA and protein expression levels of the GFP gene were significantly increased compared to those under short NDR. Furthermore, reducing the long NDR by 80 bp (NDR\(-\)) resulted in a significant reduction in the level of GFP gene expression. However, lengthening the short NDR length by 80 bp (NDR+) significantly increased the expression level of the GFP gene. This is attributed to the fact that longer NDR facilitates transcription factor (TF) binding without energy expenditure, thereby enhancing transcription efficiency. In contrast, shorter NDR necessitates the disassembly or sliding of the \(-1\) nucleosome to accommodate TF binding, which consumes energy and reduces the transcription efficiency. These results demonstrate that the expression system constructed using the \(\pm 1\) nucleosome and NDR sequences can effectively modulate exogenous gene expression, offering a novel design approach for transgene expression control.  
Saccharomyces cerevisiae; \(\pm 1\) nucleosome; nucleosome-depleted region; transgene; gene regulation; gene expression +
Footnote †: journal: Applied sciences  
1
# Hardware Reliability Analysis of a Coal Mine Gas Monitoring System Based on Fuzzy-FTA
## 3 Reliability Analysis Process of Gas Monitoring System Based on Fuzzy-FTA
### Quantitative Analysis of Fault Tree  
In the framework of Fuzzy-FTA, and based on the theory of fuzzy sets, the L-R fuzzy numbers are used to represent the occurrence probability of basic events, based on which the fuzzy fault tree is analyzed and operated.
## 3 Results
### Identification of VOCs Entitled by Naganishia  
The six identified VOCs emitted by _Naganishia_ sp. corresponded to 3-methylbutyl acetate, ethylbenzene, 1-ethyl-3-methylbenzene, 2-phenylethanol, 2-heptanone and (5\(E\))-6,10-dimethylundeca-5,9-dien-2-one (Figure 2). Retention times, reverse similarity indexes (RSI), m/z values, and CAS Registry Number are listed in Table 2. The emission kinetics of these VOCs showed that 2-phenylethanol was the most abundant compound, and its emission increased over time (Table 3).  
Figure 1: Neighbor-joining tree based on fungal internal transcribed spacer (ITS) sequences of _Naganishia_ species, _Naganishia_ sp. Ec2 (GenBank accession number OP880438.1) and _Filobasidium oriense_ as the outgroup. Numbers labeled at each node indicate the bootstrap value (%) from 1000 replicated.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**RT** & **RSI** & **Identified Compound** & _m/z_ & **Molecular Formula** & **CAS Registration Number** \\ \hline
14.31 & 935 & ethylbenzene & 106.1 & C\({}_{8}\)H\({}_{10}\) & 100-41-4 \\
14.82 & 906 & 3-methylbutyl acetate & 130.2 & C\({}_{7}\)H\({}_{14}\)O\({}_{2}\) & 123-92-2 \\
15.40 & 862 & 2-heptanone & 114.2 & C\({}_{7}\)H\({}_{14}\)O\({}_{1}\) & 110-43-0 \\
18.41 & 905 & 1-ethyl-3-methylbenzene & 120.2 & C\({}_{9}\)H\({}_{12}\) & 620-14-4 \\
24.07 & 946 & 2-phenylethylethanol & 122.2 & C\({}_{8}\)H\({}_{10}\)O & 60-12-8 \\
34.36 & 930 & (5E)-6,10-dimethyl-5,9-undecadien-2-one & 194.3 & C\({}_{13}\)H\({}_{22}\)O & 3796-70-1 \\ \hline \hline \end{tabular}  
RT: retention time (min), RSI: reverse similarity index, _m/z_: Molecular peak mass/mass ratio, CAS Registry Number: NIST Database Identification Number. \(n\) = 3.  
\end{table}
Table 2: Main volatile compounds emitted by _Naganishia_ sp.  
Figure 2: Structure of the volatile organic compounds emitted by _Naganishia_ sp. Compounds were captured by HS-SPME and identified by GC-MS/MS.
## 3 Model Formulation
### Operating Cost (\(C_{o}\))
#### 3.2.4 Feeder Bus Operating In-Vehicle Cost (\(C_{oiF}\))  
The feeder bus operating in-vehicle cost (\(C_{oiF}\)) is dependent on the travel time and round trip distance. \(C_{oi}\) (for bus or train) is formulated based on the running cost (\(C_{roi}\)) and the dwell cost (\(C_{doi}\)). The running cost for the bus is formulated according to the round trip distance against the rail, which is the round trip time. It is assumed that the stop delay time incurred at bus stops and intersections should be taken into consideration.  
As explained in Section 3.1.3, the average cost of dwell time was defined by demand multiplied by the passenger boarding and alighting rate. Furthermore, these costs were also determined similarly in Section 3.1.3. Thus, the \(C_{oiF}\) for feeder bus route \(k\) can be formulated as given in the following equations:  
\[C_{oiF} =C_{roiF}+C_{doiF} \tag{13}\] \[C_{roiF} =\lambda_{l}\left(2F_{k}\times L_{K}\right)\] (14) \[C_{doiF} =\lambda_{l}\left(Q_{k}\times t_{dF}\right) \tag{15}\]  
The derivation of \(C_{doiF}\) is discussed later in Appendix A.
# Research on the High Reliable Electromagnetic Stirring Power Supply
## 2 Materials and Methods  
### The Structure and Principle of High-Reliability Electromagnetic Stirring Power Supply  
The electromagnetic stirrer makes the use of electromagnetic induction to push the liquid metal melt to flow at a high temperature. When a low-frequency current is applied to the electromagnetic stirrer, a magnetic field is generated. This magnetic field passes through the molten metal and produces relative motion with the molten metal, which changes the magnetic flux in the molten metal and induces internal induction electromotive force and current. The induced current interacts with the magnetic field to generate electromagnetic force, which leads to the stirring of the molten metal by promoting directional flow. Thus, it is obvious that electromagnetic stirring can achieve the non-contact stirring of liquid metal by means of electromagnetic force. The stirring intensity can be adjusted by changing the magnitude of the current and the stirring direction is varied according to the current phase, which satisfies the requirements of different processes and different alloys. The frequency of the current is decided by the size of the furnace and the structure of the electromagnetic stirrer. Its value is generally between 0.1 to 10 Hz.  
The topology of the high-reliability electromagnetic stirring power supply is shown in Figure 1. The main circuit is composed of four parts. The first part is the front-stage three-phase voltage-type rectifier circuit. The second part is the back-stage two-phase quadrature inverter circuit. The third part is the reconstruction topology circuit, and the fourth part is an electromagnetic stirrer which acts as the load. \(u_{sat}\), \(u_{sb}\) and \(u_{sc}\) are the grid voltage. VR\({}_{1}\)-VR\({}_{6}\) are six IGBTs which form the three-phase rectifier circuit. The DC side capacitance, \(C\), plays the role of filtering energy storage. TR\({}_{13}\), TR\({}_{35}\), TR\({}_{\text{wT}}\), and TR\({}_{\text{W}}\) are triacs, which form a reconstructed topology circuit. The IGBTs, namely V\({}_{1}\)-V\({}_{6}\), constitute the back-stage inverter circuit. The front-stage rectifier circuit converts the three-phase alternating current into a stable DC voltage, and supplies power to the back-stage inverter circuit. The back-stage inverter circuit inverts the direct current into alternating current and outputs the desired low-frequency current to drive the electromagnetic stirrer. The back-stage inverter circuit is prone to failure once connected to the load. When an open circuit fault occurs in the IGBT of the back-stage inverter circuit, it can easily judge the faulty switch by the fault diagnosis algorithm. Then the inverter topology can be reconstructed to achieve fault-tolerant operation and greatly improve the reliability of the electromagnetic stirring power supply.
# Evaluation of Ionospheric Delay Effects on Multi-GNSS Positioning Performance
## Materials and Methods
### Ionospheric Correction Models
#### Gim  
The GIM assumes that the ionosphere is composed of a thin spherical layer at a height of 450 km above the earth's surface [19]. The GIM provides vertical TEC (VTEC) at certain grid points over the globe. The GIM reconstructed from the predicted and actual measured datasets can be utilized for real-time and post-processed applications, respectively. The vertical electron content at the ionospheric pierce point (IPP) location is generally obtained by interpolation of VTEC values from four neighboring grid points. Hence, space and time interpolations are necessary to obtain the VTEC with high accuracy. The VTEC at an IPP needs to be firstly interpolated between two consecutive maps and then mapped to the slant ionospheric delay. The method for the interpolation and mapping of GIM is explained in detail in [20].
## 4 Calculation Method of Equal Load Factor for Dual-Input Counter-Rotating Gearbox
### Uniform Load Performance Test  
The uniform load coefficient was calculated to be 1.241 according to the backlash, and the gear G\({}_{l2}\) and G\({}_{r2}\) torque values were then measured using a stress-torque sensor with strain gauges applied to the two input shafts at the corresponding two measurement points. The measurement principle is shown in Figure 12.  
In Figure 12, 1 is the signal acquisition system; 2 is the PC; 3 is the gear box; 4 is the strain sensor; 5 is the speed and torque sensor; 6 is the driving motor; 7 is measurement point 1; and 8 is measurement point 2.  
The results of the torque measurements at measurement point 1 and measurement point 2 for various input conditions are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Same Torque Input** & **Measurement Point 1** & **Measurement Point 2** &
\begin{tabular}{c} **Mean Value of** \\ **Uniform Load Factor** \\ **K\({}_{P}\)** \\ \end{tabular} \\ \hline
100 & 0 & 159.255 & / \\ \hline
150 & 0 & 273.940 & / \\ \hline
200 & 68.417 & 133.560 & 1.323 \\ \hline
250 & 85.695 & 164.660 & 1.315 \\ \hline
300 & 103.664 & 196.106 & 1.308 \\ \hline
350 & 123.015 & 228.243 & 1.300 \\ \hline
400 & 140.708 & 259.344 & 1.296 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Multi-case torque measurement results.  
Figure 12: Gearbox equal load test schematic.  
The uniform load coefficient test data are shown in Figure 13, and the relationship between the input torque and the average value of the uniform load coefficient is shown in Figure 14.  
From Figure 14, it can be seen that the maximum system uniform load coefficient is 1.323, and with a theoretically calculated equalization factor of 1.259, this result exceeds the theoretical uniform load coefficient by 0.064, and the test-measured uniform load coefficient exceeds the theoretical value by 5.08%. The error was 6.61% compared to the uniform load coefficient calculated from the measured backlash. According to the experimental data, it can be seen that the larger the input torque is, the lower the equal load coefficient is. The difference between the elastic deflection angles of the two branches decreases as the torque increases, resulting in a more uniform load distribution of the system. Due to the input  
Figure 13: (**a–e**) Graph of the test data of the uniform load coefficient.  
torque limitation, the system load equalization performance can be improved by reducing the elastic shaft torsional stiffness of the branch with a large backlash.
# An Experimental Tethered UAV-Based Communication System with Continuous Power Supply
## 3 Methodology
### Elements for the Prototype
#### 3.2.2 Comparative Analysis and Selection of IP Telephony Software  
Table 4 shows a summarized comparison of three types of software for the implementation of IP telephony: (i) 3 CX (version 20.0), (ii) FreePBX (version 17), and (iii) Asterisk (release 22.4.1). After analyzing the characteristics of software solutions, we concluded that FreePBX is the best suited to the requirements of the prototype. This is because it is relatively easy to install, offers the necessary functions for the UAV-enabled communications system, and has a wide availability of information and resources online, including blogs and tutorials that facilitate its use and installation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Features** & **3 CX** & **FreePBX** & **Asterisk** \\ \hline
**Open-source** & Yes & Yes & Yes \\ \hline
**Raspberry Pi** & & & \\
**Compatibility** & & & \\ \hline
**Documentation and** & & & \\
**Support** & Moderate (free version) & Extensive and active & Extensive and active \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of software features for implementing the IP telephony service.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Features** & **Raspberry Pi 4B** & **Arduino Uno** & **ODROID C4** \\ \hline
**Processor** & Quad-core Cortex-A72 at 1.5 GHz & ATmega328P (8-bit) & Amlogic S905X3 (Quad-core Cortex-A55 at 2.0 GHz) \\ \hline
**RAM** & 2/4/8 GB LPDDR4 & 256 MB–1 GB & 4 GB DDR4 \\ \hline
**VoIP Compatibility** & Compatible (e.g., Asterisk, FreePBX, FreeSWITCH) & Not compatible & Compatible with Asterisk \\ \hline
**Wi-Fi Module** &
\begin{tabular}{c} Dual-band \\ (2.4/5 GHz)-integrated \\ \end{tabular} & External module required & Needs an external adapter \\ \hline
**Ports and Interfaces** & \
\begin{tabular}{c} GPIO, USB 2.0/3.0, HDMI, \\ Ethernet \\ \end{tabular} & GPIO and USB &
\begin{tabular}{c} GPIO, USB 3.0, HDMI, \\ Ethernet \\ \end{tabular} \\ \hline
**Voltage** & 5 V input & 5 V input & 5 V input \\ \hline
**Power Consumption** & 3–5 W & 0.5–1.5 W & 5 W maximum \\ \hline
**Size and Weight** & 85.6 \(\times\) 56.5 mm, 46 g & 68.6 \(\times\) 53.4 mm, 25 g & 85 mm \(\times\) 56 mm \(\times\) 1.0 mm, 59 g \\ \hline
**Operating Systems** & Raspberry Pi OS, Ubuntu, & \multirow{2}{*}{Arduino IDE} & Armbian, Android, \\  & among others & & Ubuntu, among others \\ \hline
**Price (USD)** & Around \$150 & Around \$20 & Around \$120 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of device features for the implementation of the IP telephony service [31; 32; 33].
## 4 Discussion  
### Possible Impacts of Crop Growth Stage on Ecosystem WUE  
In the present study, the seasonal dynamic trends of GPP in the WWE were bimodal, while they were unimodal in the AFE; such obvious differences might be caused by different water and carbon uptake capacities of plants at different growth stages (Figures 2 and 3). As a result, the ecosystem WUE varied with different seasons within the year (Figure 7). During the wintering stage (December to the next February), ET and GPP were very low because of the dormancy of the crops [20]. Afterwards, with the increasing temperature and vigorous crop growth, ET and GPP increased quickly to the maximum of the year. A dual relationship between GPP and ET appeared in Figure 6, which is due to the decreased ecosystem productivity during the fallow period after the wheat harvest, but the ET is still high under high temperatures. However, the overall trend of the AFE was less affected by the growth and harvest of the winter wheat, indicating that the source area was much larger when the observation height increased from 2 m to 32 m [35; 36]. The maximum WUE and GPP appeared at different times, and the maximum WUE occurred at lower GPP stages when the temperature was nearly 0 \({}^{\circ}\)C. This kind of inconsistency might occur because the trade-off between carbon assimilation and transpiration of the WWE and AFE was influenced by soil water limitation or drought, which is consistent with the results of [PERSON] et al. (2017) [49]. The relationship between crop biomass formation and water consumption should be like this, but it may also be related to crop type.
## 2 Materials and Methods  
### Study Area  
The Yangtze River is the largest river in China, with a total length of nearly 6400 km. The YRB (24\({}^{\circ}\)30\({}^{\prime}\)-35\({}^{\circ}\)45\({}^{\prime}\) N and 90\({}^{\circ}\)33\({}^{\prime}\)-122\({}^{\circ}\)25\({}^{\prime}\) E) extends from the eastern part of the Tibetan Plateau to the East China Sea, encompassing an area of nearly 1.8 \(\times\) 10\({}^{6}\) km\({}^{2}\), which constitutes approximately 18.8% of China's total land area (Figure 1a). The YRB is characterized by diverse topographies, intricate river networks, and numerous tributaries. The YRB can be divided into 12 water resource regions according to the national water resources management zoning, which are the Jinsha River Basin above Shigu (JSJ-1), the Jinsha River Basin below Shigu (JSJ-2), the Mintuo River Basin (MTJ), the Jialing River Basin (JLJ), the Wu River Basin (WJ), the Yibin-Yichang section of the Yangtze River mainstream (UM), the Dongting Lake Basin (DTL), the Han River Basin (HJ), the Poyang Lake Basin (PYL), the Yichang-Hukou section of the Yangtze River mainstream (MM), the Yangtze River mainstream below Hukou (LM), and the Taihu Lake Basin (TL) (Figure 1a).  
The YRB exhibits a typical subtropical monsoon climate, with a decline in both temperature and precipitation from the southwest to the northwest. The average annual temperature varies from 12.6-28.3 \({}^{\circ}\)C, and the annual precipitation varies between 500 to 2500 mm [42; 43; 44]. The distributions of precipitation, temperature, and shortwave radiation of the YRB during 2000-2018 were shown in Figure 1b-d. It can be observed that precipitation and temperature in the YRB both exhibited decreasing patterns from the upper reaches to the lower reaches (from northwest to southeast). This is particularly evident in the lower reaches of the Yangtze River, where precipitation is often greater than 500 mm. However, the shortwave radiation showed the opposite pattern, with a decreasing trend from the upper reaches to the lower reaches.  
The YRB boasts abundant vegetation resources, with forest reserves constituting almost 25% of the entire country's forested areas. The source region and upper reaches of the YRB are mainly characterized by alpine meadows and natural grasslands. The middle reaches of the YRB are primarily covered by deciduous broad-leaved forests and evergreen broad-leaved forests. Forests and savannas are primarily distributed in regions with favorable water and heat conditions. Cropland is extensively distributed across the middle and downstream plain regions (Figure 1e). Over the past decades, the vegetation cover in the YRB has experienced significant changes. Initiatives such as ecological afforestation projects, notably the Grain for Green Program, have facilitated the shift from agricultural land to forested areas and grasslands [8]. However, the dense population and industrial presence along the Yangtze River, particularly in the Yangtze River Delta (YRD), present a challenge to the available space for vegetation expansion.
# A Tri-Satellite Interference Source Localization Method for Eliminating Mirrored Location
## 1 Introduction
Therefore, it is urgent to propose an effective and robust localization method in which the mirrored location results are eliminated. Aiming to address the problems above, this paper proposes a method to eliminate mirrored locations in tri-satellite TDOA localization based on the TDOA measured at multiple moments. First, the TDOA measure model based on cross-ambiguity function (CAF) is established. Then, a multi-moment TDOA localization equation set is derived based on the World Geodetic System (WGS)-84 earth ellipsoid model. The location results near the correct location of the interference source is directly obtained through the TDOA measurement data at multiple moments using the Newton iteration method. Finally, the precise iterative localizations are obtained using the multi-moment location result as the initial value in the iterative localization moment by moment. This method can obtain more accurate location results on the basis of eliminating the mirrored location. At the same time, it still has good robustness under the conditions of large deviation of the prior initial value and large TDOA measurement error. The paper is organized as follows: Section 2 introduces the TDOA/FDOA measure model based on the CAF for satellite localization. Section 3 provides the multiple-moment TDOA localization method. Section 4 provides the proposed tri-satellite localization method. In Section 5, simulations are performed to validate the performance of our methods as well as the real data result analysis. We conclude this paper in Section 6.