esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Characteristics of Microseismic Activities during Excavation of the Diversion Tunnel
### Characteristic Parameter Analysis of Microseismic Activities in Special Periods
#### 4.3.2 Magnitude-Frequency Relationship (\(b\)-Value)  
[PERSON] and [PERSON] (2018) applied a linear fit to the relationship between earthquake magnitude and frequency and proposed the well-known Gutenberg-Richter law (GR law). The formula is as follows:  
\[\lg N=a-bm_{\mathrm{M}} \tag{1}\]  
where \(m_{\mathrm{M}}\) is the earthquake magnitude; \(N\) is the number of seismic events with a magnitude greater than or equal to \(m_{\mathrm{M}}\); and \(a\) and \(b\) are two constants that can be obtained by linear fitting for a given set of data. The parameter \(b\) has a special physical meaning that reveals the proportion of events with different magnitudes. [PERSON] and [PERSON] (2018) studied the magnitude-frequency characteristics of microseismic activities during mining and concluded that microseismic activities caused by fault slips tend to have smaller \(b\)-values (generally less than 0.8), while the \(b\)-values of microseismic activities induced by blasting in mining are relatively high (1.2 to 1.5).  
Similarly, the GR law is applicable to microseismic events in engineering. A linear fit was applied to the magnitude and frequency of the microseismic events from 20 October to 10 November 2020 at an interval of \(\Delta\)M = 0.05. Figure 9 shows the relationship between magnitude and frequency. The \(b\)-value in this period is 1.0, which is between the typical \(b\)-values of the two types of microseismic activities. It means that there are microseismic activities caused by blast-induced stress migration and by slips on the structural plane in this period, which is consistent with the site working conditions.  
Figure 8: Percentage distribution of \(E_{\mathrm{S}}/E_{\mathrm{P}}\) values (20 October 2020 to 10 November 2020).  
Figure 9: Relationship between magnitude and frequency of microseismic events (20 October 2020 to 10 December 2020).
## 2 Design Method of Feedback Controller
### Control Strategic Function  
Use a specified measured secondary path transfer function and a feedback controller with parameter vector \(X_{1}\) to build a feedback control strategic function. This procedure is denoted as method 1:  
\[\begin{array}{ll}J_{1}(X_{1})&=\|S(X_{1})w_{1}(\omega)\|_{2}^{2}+\gamma_{1} \{U[|L(X_{1})|-L_{\text{AM}}]+U[L_{\text{PM}}-|\text{phase}(L(X_{1})|)]\}\\ &+\gamma_{2}U[w_{2}(\omega)\|S(X_{1})\|_{\infty}-1]\end{array} \tag{15}\]  
Considering the uncertainty of the secondary path, use the mean of multiple measured secondary path transfer functions and a feedback controller with parameter vector \(X_{2}\) to rebuild the feedback control strategic function with the uncertainty constraint. This procedure is denoted as method 2:  
\[\begin{array}{ll}J_{2}(X_{2})&=\|S_{0}(X_{2})w_{1}(\omega)\|_{2}^{2}+\gamma_ {1}\{U[|L_{0}(X_{2})|-L_{\text{AM}}]+U[L_{\text{PM}}-|\text{phase}(L_{0}(X_{2}) |)]\}\\ &+\gamma_{2}U[w_{2}(\omega)\|S_{0}(X_{2})\|_{\infty}-1]+\gamma_{3}U\Big{[}\Big{|} \Big{|}\frac{|L_{0}(X_{2})w_{2}(\omega)/\delta|}{|L_{0}(X_{2})-1|-\chi}\Big{|} _{\infty}-1\Big{]}\end{array} \tag{16}\]  
where \(U(x)\) is the unit step function  
\[U(x)=\left\{\begin{array}{ll}1&x\geq 0\\ 0&x<0\end{array}\right. \tag{17}\]  
In Equation (16), the first term on the right side represents the amplitude of the sensitivity function, indicating the noise reduction performance. It reaches the minimum when the noise reduction is equal to the frequency characteristic of primary noise \(w_{1}(\omega)\), and the best feedback reduction is achieved.  
Figure 2: Nyquist plot for system uncertainty.  
The second term on the right side of Equation (16) represents the constraint on the Nyquist stability. If the open-loop function does not meet the conditions regarding amplitude margin \(L_{\text{AM}}\) or phase margin \(L_{\text{PM}}\), this term is multiplied by a penalty factor \(\gamma_{1}\), which is a positive real number.  
The third term pertains to the constraint on waterbed lift of the mean of secondary paths. If the noise enhancement exceeds a preset upper limit \(1/w_{2}(\omega)\), this term is also multiplied by another positive real number penalty factor \(\gamma_{2}\).  
The fourth term relates to the uncertainty constraint of the secondary path. If the constraint is broken through, this term is multiplied by a positive real number penalty factor \(\gamma_{3}\). If a single specified secondary path design is used, this term of the strategic function is omitted.  
In this study, the IGWO algorithm is used to solve the control strategic function. The initial parameter range can be customized based on experience, thus offering good performance in terms of solution accuracy and convergence speed. It is suggested that the optimization process should be repeated multiple times to find the global optimized filter coefficients.
## 4 Results and Discussion
### Effectiveness Analysis of Students' Coding Literacy
#### 4.2.1 Analysis of Students' Programming Abilities  
This study examined the students' programming abilities. Five types of questions were planned, including simple calculation, simple judgment, multi-condition judgment, repetitive structure, and comprehensive applications. The analysis results are shown in Table 4.  
The \"Levene test equation of error variance\" was first examined, and the F values were 0.166 (\(p\) = 0.684), 0.059 (\(p\) = 0.808), 0.514 (\(p\) = 0.475), 0.325 (\(p\) = 0.570), and 2.067 (\(p\) = 0.153). The significance level was greater than 0.05, indicating that there was no significant difference in the error variation of the two groups according to the variables, and the two groups were thus homogeneous. Therefore, covariate analysis could be performed. After reviewing the \"test of the inter-subject effect items\", it was found that the t values of \"simple calculation\" and \"simple judgment\" were \(-\)1.381 (\(p\) = 0.169) and \(-\)1.830 (\(p\) = 0.069), respectively, and that there was no significant difference. The t values of \"multi-condition judgment\", \"repetitive structure\", and \"comprehensive applications\"were \(-2.037\) (\(p\) = 0.043), \(-2.039\) (\(p\) = 0.043), and \(-2.056\) (\(p\) = 0.042), respectively, and the significance of each of these values was less than 0.05, thus achieving a significant difference. The adjusted average of the experimental group's \"multi-condition judgment\" was 4.36, which is higher than the control group's 4.10. The adjusted average of \"repetitive structure\" was 4.48, which is higher than the control group's 4.23, and the adjusted average of \"comprehensive applications\" was 4.21, which is higher than the control group's 3.92.  
The results showed that the experimental student group exhibited a significantly better programming performance in \"multi-condition judgment\", \"repetitive structure\", and \"comprehensive applications\" in the DBL Arduino project course than the control group did. This is consistent with the results of [PERSON], [PERSON], and [PERSON] (2018). However, there was no significant difference between the two groups in \"simple calculation\" and \"simple judgment\".
# Study of Cyclic Plasticity and Creep Ratchet Behavior of PTFE
## 3 Results and Discussion
### Extraction of Creep Strain from Ratchet Strain  
Assuming that \(S_{\text{max}}\) and \(S_{\text{min}}\), respectively, are the maximum and minimum stresses of the triangular wave pulsing cyclic loading of the test, the stress variation for each cyclic loading is shown in Equations (6) and (7).  
\[S(t)=S_{\text{min}}+\frac{2t}{T}(S_{\text{max}}-S_{\text{min}}),t\in[0,\frac{T }{2}]. \tag{6}\]  
\[S(t)=S_{\text{max}}+(1-\frac{2t}{T})(S_{\text{max}}-S_{\text{min}}),t\in[\frac{ T}{2},T]. \tag{7}\]  
The upper and lower limits of the integration time correspond to the time of maximum stress and the creep stress threshold in cyclic plasticity, respectively. The cyclic creep strain generated by each cyclic loading can then be expressed as Equation (8).  
\[\begin{array}{l}\varepsilon=\text{A}\int_{0}^{\frac{T}{2}}[S_{\text{min}}+ \frac{2t}{T}(S_{\text{max}}-S_{\text{min}})-S_{\text{th}}]^{\text{n}}\text{ exp}(-\text{Q}_{\text{a}}\text{ /RT})\text{dt}\\ +\text{A}\int_{\frac{T}{2}}^{T-\text{t}_{0}}[S_{\text{max}}+(1-\frac{2t}{T})( S_{\text{max}}-S_{\text{min}})-S_{\text{th}}]^{\text{n}}\text{exp}(-\text{Q}_{ \text{a}}\text{ /RT})\text{dt}\end{array} \tag{8}\]  
where \(t_{0}\) is the time corresponding to \(S_{\text{th}}\), when the loaded minimum stress value \(S_{\text{min}}\geq S_{\text{th}}\), t\({}_{0}=0\). Substituting the creep equations for different temperatures into the above equation yields the value of creep in the cyclic loading at each temperature and each stress level, as shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \(T\)\({}^{\circ}\)C & \(n\) & \(S_{\text{th}}\)/MPa & \(A\) & \(Q_{\text{a}}\)\(R\)\({}^{-1}\)/K \\ \hline
23 & 5.3 & 1.70 & \(1.32\times 10^{-9}\) & 82.3 \\
50 & 3.0 & 0.75 & \(6.18\times 10^{-8}\) & 41.9 \\
100 & 2.3 & 0.62 & \(5.52\times 10^{-7}\) & 39.4 \\
150 & 2.1 & 0.45 & \(7.11\times 10^{-7}\) & 36.8 \\
200 & 1.8 & 0.22 & \(5.48\times 10^{-6}\) & 30.4 \\ \hline \end{tabular}
\end{table}
Table 3: Creep parameters at each temperature.  
At various temperatures and cyclic stress levels, the maximum percentage of creep strain in the total strain for 200 cycles of loading does not exceed 7%. At the same creep stress level, the percentage of creep strain in the total cyclic plastic strain at 23 \({}^{\circ}\)C remains greater than that at 50 \({}^{\circ}\)C, which is related to the different types of crystalline phases of PTFE below and above 30 \({}^{\circ}\)C. The percentage of creep strain begins to rise with the temperature when it reaches 100 \({}^{\circ}\)C. Evidently, the creep deformation is more sensitive to average stress change.
# Spatio-Temporal Variations of Ecosystem Water Use Efficiency and Its Drivers in Southwest China
## 2 Data and Methods
### Data Sources and Processing  
#### 2.2.1 Remote Sensing Data  
The ecosystem WUE was calculated with the PML-V2 product with GPP and ET and a spatial resolution of 500 m. This product uses the same forcing data and couples the two internal processes of GPP and vegetative transpiration with high accuracy [18]. In this study, the Google earth engine platform was downloaded ([[https://code.eartheengine.google.com/?asset=projects/pml_evapotranspiration/PML/OUTPUT/PML_V2_8](https://code.eartheengine.google.com/?asset=projects/pml_evapotranspiration/PML/OUTPUT/PML_V2_8) day_v014]([https://code.eartheengine.google.com/?asset=projects/pml_evapotranspiration/PML/OUTPUT/PML_V2_8](https://code.eartheengine.google.com/?asset=projects/pml_evapotranspiration/PML/OUTPUT/PML_V2_8) day_v014) (accessed on 1 December 2022)) and preprocessed to obtain the annual WUE from 2003 to 2017, as calculated by the ratio of GPP and ET.  
EVI data from 2003 to 2017 were obtained from the MOD13A1 product ([[https://lpdaac.usgs.gov/products/mod13](https://lpdaac.usgs.gov/products/mod13) alv006/]([https://lpdaac.usgs.gov/products/mod13](https://lpdaac.usgs.gov/products/mod13) alv006/) (accessed on 1 December 2022)), which has a temporal resolution of 16 days and spatial resolution of 500 m. To remove cloud contamination, data were reconstructed with the harmonic analysis of time series method [19] and corrected with quality control files. Pixels affected by clouds and noise were filled with reconstructed data. Finally, monthly EVI data were obtained with the maximum value composite method and annual data were obtained by averaging the monthly EVI for each year.
## 5 Simulation and Performance Evaluation
### Convergence of the MCC-ACO  
In order to verify the fast convergence characteristics of MCC-ACO, under the same condition, we evaluated the calculation time and number of nodes deployed in MCC-ACO of UASNs with different grid sizes and compared this with the results of the standard ant colony algorithm (ACO). In order to eliminate the random effects of the experiment, the final result was taken from the average of 30 experiments.  
In general, due to the evolutionary constraints of the standard ACO, many iterations are required to generate the optimal solution. However, as search rules improve, MCC-ACO is able to obtain an optimal solution with fewer iterations, with fewer nodes to deploy. We compared the MCC-ACO's computation time and number of node deployments with the standard ACO calculation time and number of node deployments at different grid sizes. The corresponding test results are shown in Table 1.  
From Table 1, it is obvious that MCC-ACO can provide better node deployment solutions than standard ACO with the increase of the grid size. We can see that with the increase of the grid size, the number of nodes deployed by the MCC-ACO scheme was much larger than that of the standard ACO scheme. This is because MCC-ACO can effectively eliminate invalid solutions. Its performance is independent of grid size and only related to the location of event distribution. The standard ACO scheme contains redundant nodes. In addition, when the grid size was less than \(8\times 8\times 8\), MCC-ACO took more computing time, while when the grid size was larger than \(8\times 8\times 8\), the computing time of standard ACO increased exponentially. This is because when the network scale is small, the solution space to be searched is small. Compared with standard ACO, the computational complexity of MCC-ACO is larger, and its performance advantage is not obvious enough. However, when the grid scale increases, the advantage of MCC-ACO becomes more obvious. In short, the convergence and solution ability of MCC-ACO are better than those of standard ACO.
## 3 Problem Formulation
### Insulation Resistance Measurement by IMD  
In the system configuration of Figure 4, complications may occur during the insulation resistance measurement using IMD, including low measurement values, false alarms, and malfunctions of the related protective relays. Further explanations are to be provided through a comparison between Figures 5 and 6. Figure 5 depicts an ungrounded system, in which IMD is connected to the DC line of a transformerless PV power generation  
Figure 4: System configuration of problem scenario.  
Figure 3: Configuration of ground potential transformer (GPT) connected ungrounded system.  
system, while in the absence of GPT connection. The current loop for IMD insulation resistance measurement is formed through the insulation impedance \(R_{e}\parallel C_{e}\) of the system, the coupling resistance \(R_{c}\), and the resistance \(R_{m}\) of the measuring circuit. The leakage current flows through the loop, which generates a voltage \(U_{m}\) across the resistance \(R_{m}\) of the measuring circuit. Hence, the combined insulation resistance of the system can be calculated. As a transformerless solar power generation system is not electrically isolated, it is possible to monitor the insulation state of the AC side.  
Figure 6 depicts a current loop according to the IMD square wave injection occurring when GPT is linked to the AC output side of the PV power generation system shown in Figure 5. Through the GPT connection, an additional current loop through the primary-side of GPT is formed (red dotted-line). Since the series resistance of the formed loop is minute compared to the existing system insulation resistance, the IMD current for insulation resistance measurement generally flows to the neutral point of the primary side of the GPT. This induces a measurement error of IMD, resulting in a low insulation resistance measurement (virtual short circuit), even when the insulation of the system is intact. Misdiagnosis of insulation failures or ground faults can release a false alarm by IMD and result in the malfunction of associated relays.
# A Sector-Matching Probability Hypothesis Density Filter for Radar Multiple Target Tracking
## 3 Sector-Matching PHD Filter
### Observation Selection  
Considering the real-time performance of the filter, it is most appropriate to remove the clutter in the low priori probability density region by using the wave gate before updating the Gaussian component. Knowing the zero-mean measurement noise covariance matrix, \(\mathbf{R}_{k}=diag\bigg{(}\Big{[}\sigma_{x}^{2}\,\sigma_{y}^{2}\Big{]}^{T}\bigg{)}\), the threshold of the wave gate is \(d(a)=a\big{\|}\big{[}\sigma_{x}\,\sigma_{y}\big{]}^{T}-[0\,0]^{T}\big{\|}\), where \(\big{\|}\ \big{\|}\) is the Euclidean distance and \(a\) is the confidence coefficient. Then, \(d^{(i)}=\|z_{k}^{(i)}-H_{k}\mathbf{m}_{k|k-1}^{(j)}\|\), \(i=1,\ldots,|Z_{k}|\), \(j=1,\ldots,J_{k|k-1}\) is computed. The values of \(z_{k}^{(i)}\) that satisfies \(d^{(i,j)}\leq d(a)\) are selected and denoted as efficient observations \(z_{k,cf}^{i}\). Then, the efficient observations set \(\mathbf{Z}_{k,cf}=\Big{\{}z_{k,cf}^{i},t_{k,cf}^{i}\Big{\}}_{i=1}^{|\mathbf{Z}_{k,cf}|}\) is obtained for the \(k\)th scanning cycle.
## 2 Experimental Test
### Model Scenarios  
The factors influencing RWT mostly in a reservoir are the property of curtains and the discharge rules besides thermal structure [31; 32]. Thus, four characteristic variables were proposed to represent the main influencing factors.  
* Curtain position (_CP_), means the distance from the curtain to the outlet, with the unit of m.
* The water-retaining proportion (_Pr_), means the proportion of retaining water depth of TCC to the overall depth of the water, with the unit of %.
* Outlet submerged depth (_OSD_), means the depth from the water surface to the centerline of the outlet, with the unit of cm.
* Released flow quantity (_RQ_), means the flow quantity released from the outlet, with the unit of m3/h.  
By comprehensive consideration of the possible flow fields of the real flow fields, model similarity scales, and conditions of construction sites for the typical reservoirs with low released temperature water problems, a series of scenarios were designed in Table 1. Three typical distances of _CP_ were selected, which were 1.0, 2.5, and 3.5 m. From an engineering view, for most of reservoirs the closer to upstream the larger river cross-section is, the more difficult the construction is, thus the place of TCC will not be very far from the outlet. Our discussion about the influence of curtain position on TCC was carried out on this basis. _Pr_ can be set from 55% to 90%. During summer and autumn, when the lower RWT is obvious, the spillway outlet is the usual way of discharge, thus _OSDs_ were set from 24 to 40 cm, while the water level maintains 90 cm during all the tests. General outflow discharged from reservoirs has a large range, the range of released water quantity we discussed should be in the suitable range of a thermal stratification reservoir troubled by low released water temperature. Thus, take the example of the Sanbanxi project, outflow changes between 125 to 870 m\({}^{3}\)/s [33], and so the _RQ_ was set from 1.6 to 11 m\({}^{3}\)/h. The tests were carried out inside a laboratory, so only air temperature was considered in meteorological conditions with a daily measurement.  
Figure 2: Reference and generalization of thermal stratification: (**a**) Vertical temperature distributions of typical reservoirs; (**b**) Generalized target temperature distributions and comparison with observed distributions.
# LoRa 2.4 GHz Communication Link and Range
## 6 Discussion
Moreover, the maximum range of 2.4 GHz Wi-Fi networks typically varies around 100 m. Thus, the outdoor range of LoRa is almost three times larger than the outdoor range of BLE 5 and more than eight times larger compared to typical IEEE 802.11 networks. This is mainly due to the lower bandwidth and data rates used in LoRa, as well as the robustness of the LoRa-modulated signal. These numbers clearly indicate the significant difference in intended applications between LoRa (such as long-range communication and localization) and Wi-Fi and Bluetooth (such as video and audio streaming). Since LoRa modulation at 2.4 GHz has a higher bandwidth than LoRa modulation at 868 MHz, the rising edge of a signal pulse can be determined more accurately. Therefore, we expect that time-based localization methods for this technology will result in lower estimation errors. However, the results in this paper show that it is not possible to achieve the same long communication ranges as LoRa at 868 MHz and with other sub-GHz LPWANs. Therefore, more LoRa receivers have to be deployed to cover wide areas, which makes it a less feasible solution to build large public networks. On the other hand, 2.4 GHz LoRa is an interesting option for both communication and localization in privately deployed networks that are purposed for asset tracking and monitoring in large warehouses, construction sites, farms, etc.
## 4 Experimental Results
### Equivalent Viscous Damping Ratio  
The equivalent viscous damping ratio (\(\xi_{i}\)) can be used to evaluate the energy dissipation capacity of a structure, which is the ratio between the dissipated energy due to viscous damping and the elastic strain energy corresponding to a load cycle [34]. Under a positive \(d\), soil-structure interactions, including both soil-abutment interactions and soil-pile interactions, should be considered. Whereas under a negative \(d\), only soil-pile interactions should be considered. As a result, the energy dissipation capacity of the GIAP specimen-soil system under a positive \(d\) is significantly larger than that under a negative \(d\). Therefore, only the energy dissipation capacity under a positive \(d\) was analyzed. The \(\xi_{i}\) was calculated using Equation (1), where \(S_{ABCD}\) (area filled with black inclined lines) represents the dissipated hysteretic energy and \(S_{OGD}+S_{OBE}\) (triangles formed by red dash lines) is the elastic strain energy obtained from the hysteresis curves, as illustrated in Figure 12(a). The larger \(\xi_{i}\), the larger the dissipated energy and the better the seismic performance of the structure.  
\[\xi_{i}=\frac{1}{2\pi}\frac{S_{ABCD}}{S_{OGD}+S_{OBE}} \tag{1}\]  
Each \(d\) has three cycles. Therefore, the average \(\xi_{i}\) between three cycles of each \(d\) was calculated. The experimental \(\xi_{i}\)-\(d\) curves for IAP and GIAP specimens are illustrated in Figure 12(b). It can be found that the \(\xi_{i}\) for IAP specimen changes from 0.18 to 0.28 with an approximate average \(\xi_{i}\) of 0.26, and the \(\xi_{i}\) for GIAP specimen changes from 0.27 to 0.36 with an approximate average \(\xi_{i}\) of 0.34. Therefore, the energy dissipation capacity in the GIAP specimen is underestimated when the girder is not considered.