esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Subject of Study and Measurement Result
Flow parameters (signals from the flow meter and the differential pressure transducer) were recorded using SANWA 5000 multimeters connected to a measuring system on a PC, using the RS-232 interface. The computer used specialized PC Link Plus software which allowed acquiring measurement data with a preset sampling time at \(\Delta t\) = 6 s. A total of 12 volume streams were tested on the described station, with streams \(q_{v}\) ranging 0.167-0.683 dm\({}^{3}\)/s (for Reynolds numbers ranging \(Re\) = 4700-19,500). The measurements were taken for a stabilized stream of flowing water, recording 12 series of observations each, and each of the recorded series was averaged from 20 single measurements. The acquisition of measurement data involved recording the following parameters: volume stream \(q_{v}\), pressure difference at the orifice \(\Delta p\), and water temperature \(T\). For all measurement series, the water temperature did not exceed 1.5 \({}^{\circ}\)C during the study. Table 1 presents the geometry of pipeline \(D\), the geometry of orifice flow-through openings \(d_{4}\), averaged results of experimental measurements (\(T\), \(q_{v}\), \(\Delta p\)), and corresponding values from calculations: the value of Reynolds numbers \(Re\) and discharge coefficient \(C\). Based on the results of experimental measurements (Table 1), a flow characteristics chart was made for the tested multi-opening orifice with module \(m\) = 0.25 (see Figure 3). The characteristics are described with a power equation with the coefficient of determination close to 1, which indicates a very good adjustment of the designated model (trend line) to the averaged results obtained from the experiment. \begin{table}
\begin{tabular}{c c c c c c c} \hline \(D\) & \(d\) & \(T\) & \(q_{v}\) & \(\Delta p\) & \(Re\) & \(C\) \\ \hline (mm) & (mm) & (\({}^{\circ}\)C) & (dm\({}^{3}\)/s) & (Pa) & (-) & (-) \\ \hline
50.20 & 12.54 & 25.30 & 0.6825 & 2289.4 & 19,479 & 0.6283 \\ \hline
50.26 & 12.53 & 25.40 & 0.6536 & 2098.9 & 18,696 & 0.6284 \\ \hline
50.37 & 12.53 & 25.40 & 0.6202 & 1891.1 & 17,740 & 0.6282 \\ \hline
50.28 & 12.52 & 25.35 & 0.5907 & 1715.4 & 16,879 & 0.6283 \\ \hline
50.31 & 12.52 & 25.25 & 0.5580 & 1533.0 & 15,909 & 0.6278 \\ \hline
50.36 & 12.51 & 25.20 & 0.5196 & 1327.5 & 14,798 & 0.6283 \\ \hline
50.26 & 12.51 & 25.10 & 0.4858 & 1160.2 & 13,802 & 0.6283 \\ \hline
50.25 & 12.52 & 25.00 & 0.4590 & 1035.0 & 13,012 & 0.6285 \\ \hline
50.40 & 12.51 & 24.95 & 0.4145 & 843.1 & 11,739 & 0.6289 \\ \hline
50.22 & 12.53 & 24.90 & 0.3456 & 581.9 & 9774 & 0.6310 \\ \hline
50.35 & 12.52 & 24.85 & 0.2537 & 310.4 & 7168 & 0.6344 \\ \hline
50.24 & 12.52 & 24.75 & 0.1666 & 130.0 & 4697 & 0.6439 \\ \hline \end{tabular}
\end{table} 
Table 1: Results of experimental measurements.
# Coverage Analysis and Efficient Placement of Drone-BSs in 5G Networks +
###### Abstract  
The integration of drones as base stations has shown to be a potential approach for the future mobile communication systems. Hence, this emerging technology is currently being investigated within the 3 GPP standardization community with the main objective of improving coverage and capacity in dense urban areas. Nevertheless, in order to provide adequate coverage for users, it is necessary to find the optimal location of the Drone-BS. This work proposes a novel approach for the Drone-BS in 5G communication systems, using the meta-heuristic algorithm. Firstly, we analyse the downlink coverage probability according to SINR by using stochastic geometry. Afterwards, we apply the Grey Wolf Optimizer algorithm in order to find the optimal Drone-BS placement under coverage probability constraint.  
done-Base Station; coverage; 5G networks; Grey Wolf Optimizer +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# Brillouin Frequency Shift Sensing Technology Used in Railway Strain and Temperature Measurement
## 2 Measurement Theory
### Brillouin Frequency Shift  
Since Brillouin scattering is inelastic scattering caused by the collision of incident light and scattered phonons, the frequencies generated at the Stokes and anti-Stokes zones are different from the original frequency of the incident, the frequencies of which comprise the Brillouin frequency shift (BFS); this can be expressed as Equation (1):  
\[v_{B} = 2 nV_{a}\ /\ \lambda \tag{1}\]  
where \(n\) is the refractive index of the optical fiber, \(V_{a}\) is the acoustic wave velocity in the optical fiber, and \(\lambda\) is the wavelength of incident light.  
[PERSON] et al., 1995, calculated the BFS center frequency [3]. They used \(n=1.46\), \(V_{a}=5945\) m/s, and \(\lambda=1.32\) um as the calculation parameters of a standard single-mode fiber. The BFS center frequency outcome was 13.15 GHz. In this test, a pumping light with a 1.55 um wavelength was used, so we obtained a BFS center frequency of 11.2 GHz.  
The research by [PERSON] et al., 1989 and [PERSON] et al., 1990 showed that the Brillouin frequency shift is highly related to the external strain [4] and temperature [5]  
Figure 1: The back-scattering in silica single-mode fiber: there are three (3) scattering frequencies in single-mode fiber against the 1550 nm incident light (following the work of [PERSON], 2014). Note that the Brillouin scattering frequency is only compounded by temperature (T) and strain (\(\epsilon\)).  
acting on a fiber, and the BFS can be quantitatively analyzed based on its linear parameters. Hence, the relationship between Brillouin frequency shift, strain, and temperature is shown in Equation (2):  
\[\begin{array}{c}v_{B}\ =v_{B0}\ +C_{\epsilon}(\epsilon-\epsilon_{0})+C_{T}\ (T-T_{0})\\ \Delta v_{B}\ =C_{\epsilon}\Delta\epsilon+C_{T}\Delta T\end{array} \tag{2}\]  
where \(v_{B}\) is the measured Brillouin frequency shift, which combines two factors of strain, (\(\epsilon\)) and temperature (\(T\)); \(v_{B0}\) is the initial value of the Brillouin frequency shift at a specific temperature; and \(C_{\epsilon}\) and \(C_{T}\) are the coefficients of strain and temperature, respectively. Here, both \(C_{\epsilon}\) and \(C_{T}\) were related to the characteristics of the outer package of the optical fiber; actual coefficients are specific to each fiber manufacturer.  
[PERSON], 2017, found \(C_{\epsilon}\) and \(C_{T}\) reference values of 0.046 MHz/\(\mu\epsilon\) and 1.07 MHz/\({}^{\circ}\)C, respectively [6]. Therefore, it can be stated that the difference of the initial and measured BFS values is the sum of the strain and temperature differences.  
In this experiment, \(C_{\epsilon}\) and \(C_{T}\) were 0.0497 MHz/\(\mu\epsilon\) and 1.07 MHz/\({}^{\circ}\)C, respectively; the constants were given by the optic cable supplier Neubrex [7] for the FN-SSL-3 model cable.  
The above mentioned BFS sensing method had been used in railway composite bogies [8] and railway bridge [9] measurement.
# Electricity Losses in Focus: Detection and Reduction Strategies--State of the Art
## 3 Methods for Identifying Power Losses
### Non-Technical Energy Losses  
Non-technical energy losses are defined as losses that directly affect the power quality of electricity grids, thereby causing significant economic problems. Consequently, ascertaining these losses is imperative for the energy sector. A significant number of researchers in the electrical, energy, and non-energy domains have explored this issue in power grids, contributing a range of methods and methodologies for identifying both technical and non-technical losses [29].  
Figure 4: Representative graph of the number of publications in the field of technical loss identification.  
Figure 5: Number of publications on the identification of technical losses classified by territories.  
Non-technical energy losses represent a domain of significant scientific interest, and the contributions of researchers in this area are presented in Figure 6 according to their respective shares. Therefore, the graph highlights the predominant scientific categories in which authors have contributed over time. The graph contains information provided by existing Scopus databases, generating a ranking according to the existing categories of papers. The predominant categories of contributions are journal articles (52.5%) and conference papers (37.4%), followed by review papers (5.1%), book chapters, textbooks, lecture notes, and other categories. The scientific interest in the field of non-technical losses is at a high point, and this research is now increasing.  
As a problem that impacts stability in power systems, there are various methods to address the issue of non-technical losses. In [30], the author proposed a methodology rooted in the mathematical metrics of AI, designated as F1--Score, for the identification and realization of a classification of these types of losses. This approach was implemented to address deficiencies in field data. The primary contribution of this work lies in the utilization of 23 distinct classifiers, with considerations given to criteria such as performance, execution time, and reliability. The employment of AI algorithms was instrumental in the identification of non-technical losses, with a dataset comprising 261,489 consumers undergoing analysis. The findings, ascertained through the utilization of this performance metric, affirm that the methodology devised by the authors engenders an accuracy of 66.5% in the field measurements and an error of 6.86% in comparison to the results simulated in the paper.
# Detecting IoT Attacks Using an Ensemble Machine Learning Model
## 5 Results and Analysis
### Cloud Layer Result Analysis
#### 5.1.2 Performance Measures  
Figure 6 shows overall performance as measured by kappa, F-measure, and the ROC area. It shows that all the models have values greater than 0.99, with model 8 providing the kappa value 0.991, the F-measure value 0.995, and the ROC area 0.999. Figure 7 shows the errors with voting as an ensemble method in terms of mean absolute error, root mean square error, relative absolute error, and root-relative squared error. Model 1, with voting, exhibits significantly fewer errors than any other model. In this model, DT, RF, and K-NN are used as base classifiers, and voting is used as an ensemble technique. In spite of this, we selected model 8 with voting to run in the fog layer  
To verify further, we calculate the performance of model 8 in terms of precision, F-measure, MCC, and PRC area (Figure 8), in addition to all other metrics. Through the Y-axis, the result is accurate to three decimal places. The most significant performance metric is MCC, which indicates how random or real the prediction is. It ranges from \(-1\) to \(1\). Model 8's values in the experiment are typically closer to \(99.99\) percent. In general, model 8 with voting is highly optimized to run on the fog layer, according to the requirements of real-time execution and excellent performance.  
Figure 8: Performance of the selected model.  
Figure 7: Errors associated with all the models.  
We found that model number 8, using K-nearest neighbor, naive Bayes, and decision trees as the base classifiers outperforms all other models with respect to execution time and performance metrics (such as kappa, F-measure, ROC, and MCC). Since time is an important factor in the selection of any model, the voting ensemble technique determines that model 8 takes the least time: 1.15 s. Additionally, kappa, F-measure, ROC, and MCC have maximum values of 6.39, 98.20, 99.60, and 96.40, respectively. There is also a mean absolute error of 7.78 percent, a root mean square error of 17.64 percent, a relative absolute error of 15.87 percent, and a root-relative squared error of 35.63 percent. Further, the root-relative squared error of model 8 is 27.94 percent, and the minimum impact is 0.6 percent. In fact, model 8 is the most time-efficient and resource-intensive model, which is why it has the greatest impact.
# Special Issue on Advanced Optoelectronic Devices and Systems: An Overview
## 2 An Overview of the Published Articles
This hardware layout can realize TEY and TFY data acquisition simultaneously and make the system have the characteristics of low noise and high SNR. On this basis, the BL07U control and data acquisition software composed of six working modules is developed, which ensures the universality of the BL07U data acquisition system and realizes the real-time visualization of data processing in the experiment process. The experimental results of the BL07U show thatthe system can simultaneously collect signals of experimental samples, standard samples, and incident photon fluxes in up to five channels. A space remote sensing system not only needs to design a high-performance photoelectric system but also needs to consider the impact of space radiation on the aircraft and the difficulty and cost of launching. In order to achieve lightweight and complete protection, the article by [PERSON] (contribution 5) introduces the light omnidirectional radiation protection method of the photon counting imaging system. The Al housing is wrapped as the first layer of radiation protection for the detector, and a shielding ring is added to the most sensitive area to provide electromagnetic shielding. In order to improve the shielding effect and reliability, the author selected the appropriate shielding material and mass ratio combination according to the distance between the shielding material and the protected object and the particle environment. Through structural modeling and material simulation analysis, the photon counting imaging system is comprehensively evaluated. The results show that the local shielding ring not only does not affect the sensitivity and the maximum count rate but also achieves the same radiation protection effect as the global shielding ring while reducing the weight requirement. The quality of the data collected by the optical scanner directly affects the accuracy of the 3D facial measurement, while the data collected by the computer can help the surgeon guide and simulate the plastic surgery. The article by [PERSON] (contribution 6) proposed a system based on structured light scanners to simulate and guide the analysis of plastic effects. In the research, the author uses the cascaded regression tree algorithm to extract the 3D face data collected by the camera and obtain the face features composed of key points. Then, several deformation operators of face mesh based on finite elements are designed. The results of the operation were quantitatively evaluated by registering the 3D scan model before and after the operation. The imaging accuracy of the system is verified by comparing it with the measurement results of a manual tape measure. At the same time, the virtual prediction software made a virtual prediction of the overall appearance of 10 experimental subjects. According to expert evaluations, most edited 3D faces meet these esthetic standards.
## 2 Research Context
The aim is to minimize both, but strategies to reduce costs do not always result in lower emissions and vice versa. The challenge lies in balancing conflicting goals and finding optimal solutions--reducing transport emissions without negatively impacting customer service or raising overall costs [24]. Current literature on green inventory management highlights the shortage of studies utilizing standardized methods for calculating emissions associated with inventory management. Accurate information on emissions and costs is crucial for effectively implementing green inventory management techniques and meeting legislative reporting requirements [5]. Finally, as declared in [51], the EU has recognized the necessity for a standardized methodological framework for calculating emissions arising from transport services. In 2012, the European Committee for Standardization introduced the first standard specifically for transport services. The standard EN 16258 established a common methodology for calculating and declaring transport service's energy consumption and GHG emissions. While EN 16258 played a significant role in harmonizing emissions from the transport sector, it was deemed insufficiently precise for providing fully comparable emissions data across different transport services. In March 2023, the new standard ISO 14083 [52], based on the previous standard EN 16258, established industry practices, and the Greenhouse Gas Protocol, was published to address the challenges of harmonizing the measurement and calculation of GHG emissions and to promote its adoption in the transport sector. Increased transparency in service performance is expected to incentivize companies to reduce GHG emissions and enhance the efficiency and sustainability of transport [51]. This research calculates GHG emissions according to the newly introduced ISO 14083 standard, which provides a common methodology for quantifying and reporting GHG emissions from the operation of passenger and freight transport chains.
## 3 Methodology
### VBM SPM Model
#### 3.1.2 SPM \(t\)-Map  
Normalized DARTEL--wrapped, modulated smooth GM and WM images were separately used to build a general linear model (GLM) to detect gray matter (or white matter) volume changes performing a voxel-wise two-sample \(t\)-test (one group being CN and the other being MCI or AD) in SPM12 using Equation (1):  
\[Y_{i}=X_{i}\beta+\varepsilon_{i} \tag{1}\]  
Here, \(Y_{i}\) is the predicted functional value for the \(i\)th participant and \(X_{i}\) (design matrix) is the vector of regressor variables for the \(i\)th participant. For the matrix test, \(X_{i}\) is set [1 0] for group 1 and [0 1] for group 2, during two-sample \(t\)-tests. \(\beta\) is a vector of parameters that varies from voxel to voxel and must be estimated after the model is built. In the two-sample \(t\)-tests, it represents the mean contrast of each group including covariates value as well. \(\varepsilon_{i}\) is a normally distributed error term with a mean of zero and a variance of unit S.D and varies across voxels. The total intracranial volume (TIV) was engaged in the design matrix as a covariate with an absolute threshold at a value of 0.2. This model was implemented using \(p<0.001\) and Gaussian random field (GRF) theory [33] for family-wise error (FWE) correction to produce a final \(t\)-map image. Here, the \(p\)-value indicates the probability of the null hypothesis being true; therefore, the lower the \(p\)-value, the better is the model. Once SPM \(t\)-map image was generated, we could concentrate ROI selection on those areas with GM volumes change detected by a voxel-based analysis using contrast as a feature as in Figures 3 and 4, for further feature extractions. We have considered the selection and  
Figure 3: Screenshot from (**a**) GUI of SPM12 for 3.1.1. and 3.1.2. (**b**) GUI of MarsBaR for 3.3. Each number arrowed in the figure represents the corresponding section in this article.  
generation of the ROI as the most crucial and result-oriented process. If the selected ROI is not chosen properly, an unfavorable result may be obtained.
## 4 Results and Discussion  
### Extraction of \"Weak\" FIV  
#### 4.1.1 Spectrum Analysis for CCF of Original Vibration and Sound Pressure Signals  
Figure 3 shows the waveform and spectrum of the original vibration and sound pressure signals measured at 1, 20, 40, and 60 min in the wear experiments. It can be observed from Figure 3a,b that the waveform of the original vibration signals was uneven and the energy of the high-frequency part was mainly distributed in the range of 4000-7000 Hz. Unlike the vibration signals, the low-frequency band dominated the spectrum of the sound pressure signals and caused a periodic variation in the waveform, as shown in Figure 3c,d. Meanwhile, the Pearson correlation coefficient was used to describe the correlation between two kinds of signals [21,22]. The Pearson coefficients were \(-0.03\), \(-0.06\), \(-0.05\) and \(-0.07\), respectively, which demonstrated that the original vibration and sound pressure signals were less correlated at each time. Moreover, a negative coefficient indicated a negative correlation, which was inconsistent with the fact that the FIS was strong when the FIV was violent. As a result, the original signals could not represent the FIV and FIS. Therefore, it was necessary to denoise the original vibration and sound pressure signals to identify the correlated components between the two to further detect the \"weak\" FIV.
## 3 Results and Discussion
### Microstructural Characteristics
The mechanical properties of the B610 CF high-strength steel base material include a yield strength of 500 MPa, an ultimate tensile strength of 620 MPa, and an elongation of 28%. The yield strength, ultimate tensile strength, and elongation of the 316L stainless steel base material are 250 MPa, 570 MPa and 45%, respectively. The tensile performance slightly improved to 98% of the 316L base metal and the elongation significantly increased from 39% to 82% of the 316L base metal when using laser welding instead of GTAW. Considering the function of hydro plant bellows to accommodate the movement and vibration of hydro plant parts, it is necessary to ensure the sufficient elongation of the joints. Fracture features were studied to further understand the significant differences in the elongation of the welded specimens between GTAW and laser welding, as seen in Figure 8. The fracturing of the three tensile specimens by laser welding occurred in the 316L stainless-steel base metal, indicating the good tensile properties of the samples welded by laser. The results of GTAW show that only one fracture was located in the 316L stainless-steel base metal, and the others fractured at the interface between the HAZ and the FZ, as shown in  
Figure 6: Microhardness distributions of welded joints: (**a**) upper region of GTAW, (**b**) upper region of laser welding, (**c**) bottom region of GTAW, and (**d**) bottom region of laser welding. Figure 8c. Insufficient fusion was detected in the bottom region of the fracture location, which was in agreement with the optical observation in Figure 3a. Figure 8d-f show the fractured surface topography in the upper region of the fracture location. The dimple features confirmed the occurrence of a ductile fracture event. A pore with a diameter of 570 \(\upmu\)m can also be seen in Figure 8e. In conclusion, the joints created by GTAW, exhibiting discontinuous and insufficient fusion and more ferrite precipitation at the HAZ, caused poor and fluctuant tensile properties. The joints fabricated by laser welding, exhibiting a full weld without visible defects and homogeneous elemental distribution in the HAZ, exhibited good tensile properties.