esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Testing Methods
### Dual-Variable Angle Variation Test  
We can see from Equation (4) that the angle change value is calculated through the displacement change value of the light spot. Therefore, we can also calculate the change value of the bivariate angle through the relative displacement value of the bivariate spot for testing.  
To calculate the relative displacement of the bivariate laser spot, we first need to convert the displacement \(S_{i}\) measured at different reflection times \(n_{i}\) and testing distance \(H_{i}\) to the uniform displacement \(S_{i}{}^{\prime}\) of the spot under the conditions of reflection times \(n\) and testing distance \(H\). Then, through vector calculation, we obtain the relative displacement value \(\Delta S\) of the bivariate laser spot. Finally, we calculate the relative angle change value of the two variables according to Equation (4).  
We assume two variables: \(O_{1}\) and \(O_{2}\); the test parameters for variable \(O_{1}\) are: reflection number \(n_{1}\) and distance \(H_{1}\), spot displacement \(S_{1}\), and the test parameters for variable O\({}_{2}\) are: reflection number \(n_{2}\) and distance \(H_{2}\), spot displacement \(S_{2}\).  
Figure 4: Measuring Principle.  
Based on the test parameters of variable \(O_{1}\), we convert \(S_{2}\) measured under \(O_{2}\), \(n_{2}\), and \(H_{2}\) conditions to \(S_{2}{}^{\prime}\) under \(O_{1}\), \(n_{1}\), and \(H_{1}\) conditions:  
\[\frac{S_{2}^{\prime}}{S_{2}}=\frac{2n_{1}{}^{2}H_{1}}{2n_{2}{}^{2}H_{2}} \tag{6}\]  
We can see from Equation (6) that the conversion of displacement only changes the numerical value and does not change the vector direction of \(O_{2}\) displacement. The relative displacement \(\Delta S\) of the dual laser spot is:  
\[\Delta S=S_{1}+S_{2}^{\prime} \tag{7}\]  
We then calculate the relative angle change value \(\Delta\theta\) of the two variables based on Equation (4), and the schematic diagram of relative displacement calculation is shown in Figure 5.
## 3 Case Study--Results
### Case No. 1  
In case no. 1 four branches \(ABCD\) (the crude preheat trains) were selected from a real-life HEN coupled with a CDU rated 110 kg/s of crude oil. Twenty-six shell-and-tube, two-pass HEs with straight tubes and floating heads are connected as schematically shown in Figures 2 and 8.  
Figure 8: Scheme of the HEN with PID-control loops 1 and 2 implemented in Simulink/MATLAB.  
Owing to limited measurement data, it was not possible to determine the relationship between the thermal resistance of fouling \(R_{f}\) (fouling factor) and time \(t\), for each HE. The measurements of temperature and mass flow were performed only at the inlet and outlet of the studied HEN but no temperature measurements were available between the HEs. In order to resolve this issue, the \(R_{f}\) values of HEs that were used in the simulation studies had been postulated by the authors on the basis of values recommended by TEMA standards (19) (see Table 1).  
As demonstrated in reference [6], such thermal resistance values may significantly affect the performance of HE control. As shown in the HEN scheme in Figure 2, the crude-oil feed stream is split in parallel branches \(A\) and \(B\) before the desalting unit and in parallel branches \(C\) and \(D\) after the desalting unit. For the two control loops with PID controllers 1 and 2 as indicated in Figure 2, the split ratios in branch pairs \(AB\) and \(CD\) are adopted as manipulated variables (which can be changed by the action of control valves \(LAB\) and \(2 AB\)), while the controlled variables are defined as the differences between the studied outlet temperatures: \(CV_{1}=T_{A}\) - \(T_{B}\), \(CV_{2}=T_{C}-T_{D}\). The control objective is to maximize heat recovery, understood as total heat flow \(Q\) transferred in the HEN, and the setpoint values of the controlled variables should be \(CV_{1}=CV_{2}=0\). In other words, when process disturbances occur, the controllers installed in the HEN are required to adjust the split ratios in network branches \(AB\) and \(CD\) to ensure that the values of the controlled variables return to zero.
## 4 Results and Discussions
### Data Acquisition
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sensor Group** & \begin{tabular}{c} **Distance to** \\ **Calibration** \\ **Standard** \\ **Surface (Top)** \\ **/mm** \\ \end{tabular} & \begin{tabular}{c} **Distance to** \\ **Calibration** \\ **Standard** \\ **Surface (Down) /mm** \\ \end{tabular} & \begin{tabular}{c} **Distance to** \\ **Test Platform** \\ **Upper Plate** \\ **/mm** \\ \end{tabular} & \begin{tabular}{c} **Distance to** \\ **Test Platform** \\ **Upper Plate** \\ **(Down)/mm** \\ \end{tabular} & \
\begin{tabular}{c} **Calibrated** \\ **Result** \\ **between Two** \\ **Sensors/mm** \\ \end{tabular} &
\begin{tabular}{c} **Spacing in Test** \\ **Platform /mm** \\ **Platform /mm** \\ \end{tabular} \\ \hline
1 & 9.533 & 6.189 & 9.527 & 6.186 & 244.278 & 259.991 \\
2 & 10.896 & 6.249 & 10.992 & 6.158 & 242.855 & 260.005 \\
3 & 10.328 & 6.171 & 10.331 & 6.181 & 243.500 & 260.012 \\
4 & 9.633 & 7.244 & 9.529 & 7.351 & 243.123 & 260.003 \\
5 & 10.200 & 7.173 & 10.204 & 7.181 & 242.627 & 260.012 \\
6 & 11.018 & 6.396 & 11.006 & 6.429 & 242.586 & 260.021 \\
7 & 10.969 & 6.786 & 10.961 & 6.789 & 242.245 & 259.995 \\
8 & 10.892 & 5.103 & 10.899 & 5.104 & 244.004 & 260.007 \\
9 & 9.990 & 4.976 & 9.982 & 4.997 & 245.034 & 260.013 \\
10 & 10.668 & 5.478 & 10.671 & 5.48 & 243.854 & 260.005 \\
11 & 10.599 & 5.388 & 10.623 & 5.357 & 244.013 & 259.993 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistics of the experimental test data.
## 1 Introduction
Additionally to this first study and as our second contribution, we have deeply examined each of the solutions according to different design guidelines. We have selected these guidelines since they are usually taken into account by the scientific/developer community [2, 9, 10] and different well-known end-users [3] in accordance with the WMSN requirements with the aim of selecting the best solution for their application. As a result of these works, none of these proposals satisfactorily solve most of the LR-WPAN requirements in mesh topologies. Under these circumstances, IEEE recently published the IEEE 802.15.5 standard, a recommendation that extends the IEEE 802.15.4 functionality to the WMSNs arena. This standard offers the user multiple valuable services, highlighting among them two efficient energy saving mechanisms. In comparison with the rest of proposals, IEEE 802.15.5 addresses the largest number of WMSN requirements and design guidelines, what makes it an appropriate candidate for a wide-range of WMSN applications. This is a clear advantage over most of the approaches studied in this paper, which are usually designed to solve some particular problem in the home, building and industrial sectors. Therefore, as a third contribution of this paper, we discuss the fundamental features and operation details of the IEEE 802.15.5 standard focusing on its advantages in comparison with the rest of commercial WMSN proposals. However, not all are benefits in this standard. In particular, some features such as the end-to-end reliability or security are not specified, and researchers and developers currently lack an available open-access implementation of this standard as well. These are the reasons why we also provide some hints aimed at laying the basis for future improvements of this standard. The rest of the paper is organized as follows: the main requirements of WMSNs are identified and outlined in Section 2. Then, in Section 3, we review the most relevant contending proposals in the WMSN area. Section 4 introduces the IEEE 802.15.5 standard and its two energy saving mechanisms. Section 5 discusses the proposals selected as a function of different design guidelines. Next, in Section 6, we highlight the benefits related to the IEEE 802.15.5 standard and point out its open research issues. Finally, Section 7 concludes the paper.
## 4 System Parameter Analysis  
Due to the inherent limitations of adiabatic approximation theory and linear response theory, the SR input signal must satisfy strict amplitude and frequency constraints, specifically requiring both parameters to be less than 1. To address this requirement, this study employed a rescaling transformation for signal preprocessing. Given that the amplitude of valve-leakage signals naturally satisfies the amplitude constraint (being significantly less than 1), only frequency scaling is necessary. For systematic parameter analysis, we constructed a synthetic sinusoidal signal with additive noise, characterized by an amplitude \(A<1\) and frequency \(f>1\) Hz. The mathematical representation of the input signal is given by  
\[S(t)=A\sin(2\pi ft)+noise(t), \tag{11}\]  
where \(noise(t)\) is Gaussian white noise, the noise intensity is 0.01, the signal sampling frequency is \(f_{\mathrm{s}}=2000\) Hz, and the sampling time is t = 3 s. In the formula, the signal strength is \(A=0.001\) and the signal frequency is \(f=10\) Hz. Initially, the controlled system parameters were fixed at \(a=b=1\). The SaRT-CMSR algorithm was employed to optimize the multi-objective parameters, including the control system parameters \(c\) and \(d\), coupling coefficient, and rescaling factor, using the cross-correlation gain as the objective function. The optimized system output was subsequently obtained. Figure 3 illustrates the time and frequency domain characteristics of the pure sinusoidal signal. Figure 4 depicts the time and frequency domain representations of the noisy signal, where the signal is entirely obscured by noise. Figure 5 presents the time and frequency domain diagrams of the output signal from the SaRT-CMSR system.  
Figure 2: SaRT-CMSR algorithm process.  
to the barrier height, the Brownian particles will jump irregularly between the potential wells, which cannot achieve the effect of noise reduction enhancement. At the same time, parameter b controls the barrier width, which has an important impact on the synergistic resonance between the signal and the system. Therefore, in this study, the parameters a and b of the controlled system were also used as the parameters to be optimized.
## 3 Results
### Statistical Tests
#### 3.4.5 Corrected Repeated (Ten-Times) 10-Fold Cross-Validation Paired \(t\)-Test
LGBM showed no significant gaps from the high-performing models on accuracy (e.g., LGBM vs. CB: \(p=0.1244\), LGBM vs. XGB: \(p=0.1893\)). However, in precision and ROC-AUC, it was eclipsed by ANN, CB, and XGB (\(p\leq 0.034\)). Its F1 performance remained on par with other classifiers except LR. CB and XGB consistently emerged as leading classifiers across most metrics. Neither displayed significant superiority over the other (all \(p\geq 0.8158\) in precision; \(p=0.9001\) in ROC-AUC), and both attained strong positions in accuracy and precision. Their out-performance of LR, RF, and SVM reached statistical significance in multiple comparisons (e.g., XGB vs. LR in precision: t = 4.0168, \(p<0.0001\); CB vs. SVM in precision: t = 4.3526, \(p<0.0001\)). Regarding ANN, no statistically significant results indicate its outperformance; however, the t-values suggest a tendency toward inferiority in opposition to CB and XGB. Overall, LR was conclusively the weakest, while CB and XGB formed a top tier alongside ANN in precision and ROC-AUC. SVM and RF excelled primarily in recall, whereas LGBM maintained competitive yet slightly less dominant results.
# Cooperative Multi-Objective Optimization of DC Multi-Microgrid Systems in Distribution Networks
## 3 Case 3
#### 5.3.3 Cooperative Multi-Objective Optimization Results  
It can be concluded from Table 6 that, under the cooperative multi-objective optimization model, the total daily economic cost \(C_{ETC}\) of MMGS is the lowest. The cost of case 4 adopting the cooperative multi-objective model is 16.3% lower than that for case 1, 13.9% lower than that for case 2, 8.6% lower than that for case 3 which only uses the economic dispatch model of the inner-loop without optimizing reactive power output of DC/AC converters. It is proved that the cooperative multi-objective optimization model improves the economy of MMGS.  
It can be concluded by analyzing the carbon emissions data in Table 7 that the total carbon emissions of the MMGS and distribution network with cooperative multi-objective optimization are the lowest among the four cases, which is 24.0% lower than that for case 1, 24.6% lower than that for case 2, and 29.8% lower than that for case 3, which does not optimize the reactive power. The economic cost of MMGS, the network loss of the distribution network, and the total carbon emission of MMGS and the distribution network were all optimized, which fully proves that the cooperative multi-objective optimization achieved the effect.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Case** & **Case 1** & **Case 2** & **Case 3** & **Case 4** \\ \hline \(C_{ETC}\) & \multirow{2}{*}{8585.7} & \multirow{2}{*}{8350.3} & \multirow{2}{*}{7862.7} & \multirow{2}{*}{7188.9} \\ Total Economic Cost of MMGS/CNY & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: The final economic cost of MMGS in four cases.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Case** & **Case 1** & **Case 2** & **Case 3** & **Case 4** \\ \hline \(E_{C}\) & \multirow{2}{*}{237.6} & \multirow{2}{*}{239.5} & \multirow{2}{*}{257.1} & \multirow{2}{*}{180.5} \\ Total Carbon Emissions/kg & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Total carbon emissions in the four cases.  
Figure 13: Increased network loss in four cases.
# Analysis and Application of Lining Resistance to Water Pressure in Tunnel through Karst Cave
## 5 Discussion
### Verification of Model Reliability  
In order to verify the reliability of the results of numerical simulation, the results of the numerical calculation were compared and analyzed with the results of the field measurements. The earth pressure and water pressure at the observation point when the thickness of grouting was 5 m in Figure 17 were extracted and compared with the earth and water pressure obtained from the field monitoring in Section 4.3; the results are plotted in Figure 25. From Figure 25a, it can be seen that the overall numerical calculation results were lower compared to the actual measured water pressure in the field, but the error was basically within 0.03 MPa. The reason for the lower calculation result is that the actual grouted stone was not homogeneous due to the limitation of the construction process on site; however, in the numerical calculation, the model was too idealized, resulting in the hydraulic pressure resistance being better than the actual situation. In Figure 25b, it is shown that, for earth pressure, the numerical simulation results were in good agreement with the field test results, and the average error was within 0.01 MPa. The above-discussed results reflect that the numerical calculation model of this paper has good reliability in general and can be used to guide field construction.  
Figure 25: Comparison of numerical calculation results and field monitoring results for different locations: (**a**) water pressure results comparison graph; (**b**) earth pressure results comparison graph.
# Single Photon Counting Performance and Noise Analysis of CMOS SPAD-Based Image Sensors
## 6 Spatio-Temporal Oversampling of Photon Counting Pixels  
As analogue SPAD pixels suffer from increasing cumulative noise at higher photon counts and the effective full well is restricted, oversampling individual frames at low photon counts provides a means to create an image of high dynamic range with low overall noise. This section addresses trade-offs, and details different methods, of spatio-temporal oversampling of photon counting pixels. The Quanta Image Sensor (QIS) framework proposed by [PERSON] [38], extrapolates the imaging trends of pixel shrink, increasing CVF, decreasing RN, decreasing full well and spatio-temporal oversampling to a concept of a SPC image sensor where a \"pixel\" is the spatio-temporal sum of multiple integrations of multiple sub-pixels (\"jots\").  
The small full well of photon counting pixels, in the order of magnitude of 100's of photoelectrons or photons, limits a sensor's dynamic range. Spatio-temporal oversampling of multiple pixels may be performed to increase the full well past a single pixel's limit. Furthermore, for DSERN photon counting pixels with cumulative noise such as the SPAD-based analogue pixels described in this paper, the level of the photon counting oversampling threshold (i.e., if pixel output \(>\)1 photon or if \(>\)2 photons, etc.) sets the noise of the oversampled output image; a higher oversampling threshold induces greater noise in the output frame image. However, this threshold is traded off against the frame rate and the oversampled full well. A signal level of N photoelectrons can be reached with less oversampled frames (and greater output frame rate) with a higher oversampling threshold of the pixel signal. By setting the threshold above the thermal and 1/_f_ noise floor, the oversampled is truly shot noise limited as little or no thermal and 1/_f_ noise accumulates.
# Design and Implementation of C-Band Large-Power Planar Butler Matrix in SRS
## 2 Principle and Design Method
### Design and Analysis
#### 2.3.3 Phase Shifter Analysis  
A phase shifter [27] with good phase flatness in the operational frequency band directly determines the accuracy of the Butler matrix output phases. The lengths of the phase shifters and the interconnecting lines have been tuned to offset the phase change caused by the stubs. The broadband 45\({}^{\circ}\) phase shifters and 0\({}^{\circ}\) phase shifters have been  
Figure 8: Simulation results of coupler S-parameters (**a**) without short-ended stubs; (**b**) with short-ended stubs.  
Figure 9: Simulation results of the crossover (**a**) E-field distribution; (**b**) S-parameters.  
The wideband 45\({}^{\circ}\) phase shifter has good transmission performance at a frequency of 3.6-4.2 GHz with a return loss greater than 40 dB and an in-band insertion loss less than 0.1 dB. The output phase difference between them is 45 \(\pm\) 1\({}^{\circ}\), and the phase flatness is superior. The simulation results are shown in Figure 11.  
The wideband 0\({}^{\circ}\) phase shifter has good transmission performance at a frequency of 3.6-4.2 GHz with a return loss greater than 30 dB and an in-band insertion loss less than 0.14 dB. The output phase difference is 0 \(\pm\) 1.5\({}^{\circ}\). The simulation results are shown in Figure 13.
###### Abstract  
Experimental results reveal that rocking shallow foundations reduce earthquake-induced force and flexural displacement demands transmitted to structures and can be used as an effective geotechnical seismic isolation mechanism. This paper presents data-driven predictive models for maximum acceleration transmitted to structures founded on rocking shallow foundations during earthquake loading. Results from base-shaking experiments on rocking foundations have been utilized for the development of artificial neural network regression (ANN), k-nearest neighbors regression, support vector regression, random forest regression, adaptive boosting regression, and gradient boosting regression models. Acceleration amplification ratio, defined as the maximum acceleration at the center of gravity of a structure divided by the peak ground acceleration of the earthquake, is considered as the prediction parameter. For five out of six models developed in this study, the overall mean absolute percentage error in predictions in repeated k-fold cross validation tests vary between 0.128 and 0.145, with the ANN model being the most accurate and most consistent. The cross validation mean absolute error in predictions of all six models vary between 0.08 and 0.1, indicating that the maximum acceleration of structures supported by rocking foundations can be predicted within an average error limit of 8% to 10% of the peak ground acceleration of the earthquake.  
geotechnical engineering; rocking foundations; earthquake engineering; soil-structure interaction; artificial neural network; machine learning +
Footnote †: journal: Applied Sciences
# Non-Ionizing Radiation Measurements for Trajectory Radars
## 3 Materials and Methods
### Measurement Scenario  
In order to analyze the NIR levels from the Bearn radar, four measurement points are chosen and named P01, P02, P03 and P04. Their positions are shown in Figure 2. They are 9, 960, 690 and 1570 m away from the radar's antenna, respectively. To locate the  
Figure 1: Flowchart of non-ionizing radiation measurement methodology for trajectography radars.  
radar's antenna and all measurement points, we use the WGS-84 and the SIRGAS 2000 coordinate systems.  
Points P01 and P02 are located at the optic designation instruments of Beam (Figure 3) and Adour (Figure 4) radars, which are operated by professionals during launching campaigns to obtain the aerospace artifact position at its visual flight phase. Point P03 is located at a space museum (Figure 5), which is one of the most visited places by tourists in the region. The last point, P04, is located at a mobile launch platform area (Figure 6), where people work during launching campaigns. These points were selected following two criteria: locations with a high probability of both exposure and presence of people.  
Three of the four measurement points (P01, P02 and P04) have a LoS propagation condition with first Fresnel zone clearance, as shown in Figures 3, 4 and 6. Otherwise, there is no LoS for the P03 link due to a shadowing caused by vegetation on the signal path, as shown in Figure 5.  
Figure 3: P01 measurement point: Beam’s antenna on the (**left**) and its optic designation instrument on the (**right**).  
Figure 2: Location of measurement points.  
All measurement points, with exception of P01, are considered to be in the far field region of the transmitter, in which the waves can be considered plane. Point P01, the closest one to the radar's antenna, is in the near field region. The limit distance between the near field and far field can be calculated by Equation (1) [4], where L is the maximum dimension of the radar's antenna, \(\lambda\) is the wavelength, \(f\) is the carrier frequency and \(c\) is the light speed (\(3\cdot 10^{8}\) m/s).  
Figure 4: P02 measurement point: [PERSON]’s optic designation instrument on the (**left**) and its antenna on the (**right**).  
Figure 5: P03 measurement point: entrance of the space museum.  
Figure 6: P04 measurement point: mobile launch platform area.  
\[d_{lim}=\frac{2L^{2}}{\lambda}=\frac{2 fL^{2}}{c} \tag{1}\]  
Assuming \(L\) equals to 4 m [2] and \(f\) equals to 5800 MHz, the calculated near far field limit distance is 618.67 m, which confirms that only P01 is within the near field region.
# Short-Text Semantic Similarity (STSS): Techniques, Challenges and Future Perspectives
## 1 Introduction
### Natural Language Preprocessing Techniques in Short Text
#### 1.2.3 Stop Word Removal  
Natural language processing filters some words from the sentences, which are known as stop words. Examples of stop words include the, a, at, so, an, and so on. These are the most frequently used terms in the language [7; 8; 9; 10; 11; 12]. Removing such words from sentences reduces the low-level information, which makes the sentences easy to understand. Moreover, removing the stop words doesn't affect the contextual meaning of sentences, as machines preserve the semantic information of words. The following is an example that illustrates an explanation of stop word removal.  
Example:  
Text: A computer is an electronic machine.  
Stop word removal: ['computer', 'electronic','machine'].