esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Verification and Validation
### Validation of the Applicability of the Proposed Model Using Real Project Data
#### 4.3.2 Evaluation of the Proposed Models  
The experts were asked to evaluate the proposed models during the second interview session. Two questions were directed to the interviewees to receive their feedback, and their answers were requested using the Likert scale (1-5), along with any comments they had. The questions and provided answers were as follows:  
**Question 1)** Do you think the parameters below (see Table 8) were included in the model in a reasonable way? Please rank from 1 to 5 (5 being the most reasonable).  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}}
**No.** & **Parameters** & **Experts’ Rankings (1–5)** \\
1. & \(\delta\) (The initial probability of being a malicious insider) & 5 \\
2. & \(p_{1}\) and \(p_{2}\) (The probability that the malicious insider is detected) & 4 \\
3. & \(\omega\) (Payoff/reward to the data defender due to detection of the malicious insider) & 5 \\
4. & \(\beta\) (Payoff/reward to malicious insider due to stolen data/compromised data) & 5 \\
5. & C\({}_{\text{cyb}}\) (Cost of cyberattack) & 5 \\ \end{tabular}
\end{table}
Table 8: Parameters used in the proposed model and the experts’ feedback.  
**Answer 1)**  
As the rankings in Table 8 indicate, the experts found the parameters used in the model reasonable. They only had a comment about the assumption of p\({}_{1}\) being always higher than p\({}_{2}\). They mentioned that this assumption is reasonable when the detection and identification of a malicious insider is considered. However, they suggested that this study could also consider the probability of detecting malicious activity. They indicated that the probability of detecting malicious activity when the insider is using a cyberattack would be higher than the probability of detecting malicious activity when the insider is using authentication.  
**Question 2)** The file security level and cost of cyberattack (\(C_{cyb}\)) are estimated as follows:  
_File security level = (Project security level) x (File accessibility level)_  
\[C_{cyb}=max\ \{\text{File security level }-\text{ malicious insider expertise level, 0}\}\]  
Are the formulae used to estimate the file security level and C\({}_{cyb}\) reasonable?  
**Answer 2)** After a round of discussion, the experts agreed that the provided formulae used in the model are reasonable. They could understand the logic behind the formulae and did not have further suggestions to improve them.
## 2 Materials and Methods
### Vegetation Indices (Test Data 2 and Test Data 3)  
Certain regions of the electromagnetic spectrum have a particular relationship with healthy green plant canopies. In the visible spectrum, chlorophyll absorbs significant amounts of energy, primarily for photosynthesis. This absorption peaks in the red and blue ranges of the visible spectrum, but chlorophyll reflects the green region, resulting in the typical green colour of most leaves. In addition, the leaf's interior structure greatly reflects the spectrum's near-infrared area [33]. This substantial disparity, especially between the absorbed energy in the red and near-infrared areas of the electromagnetic spectrum, has been the subject of several efforts to construct quantitative measures of vegetation status using remotely sensed images. Vegetation indices (VI's) have been used in various scenarios to evaluate green biomass and as proxies for global environmental change, particularly in drought and land degradation risk assessment [34; 35; 33]. VI's can be categorised into three groups: (a) slope based, (b) distance based, and (c) orthogonal transformation vegetation indices [33]. The distribution of vegetation pixels on a two-dimensional graph (or bi-spectral plot) of red versus infrared reflectance should be examined to clarify these differences, where a high portion of biomass is presented with high values in the Infrared band [36] (Figure 4). 1. Slope-based vegetation indices (SBVI) are derived by a combination of red and infrared channels (Table 2) and presented as mathematical combinations that emphasise the distinction between vegetation spectral response patterns in the red and near-infrared parts of the electromagnetic spectrum [33];
2. Distance-based vegetation indices (DBVI) attempt to neutralise the effect of soil brightness in sparse vegetation areas (Table 2), and they are derived from the Perpendicular Vegetation Index (PVI), which includes the perpendicular distance between each pixel and the soil line [37] (Figure 4). Original PVI is enhanced with three different indices: PVI1 [38], PVI2 [39], and PVI3 [40] to improve its performance;
3. Orthogonal transformation vegetation indices (OTVI) are created by applying a transformation on the existing spectral bands to generate a new set of bands that are not in  
Figure 4: Red and near-infrared pixel distribution. correlation (Table 2). A green vegetation index band may be generated within this new set of bands [33]. The 23 specific indices (Table 2) are chosen because they capture critical spectral responses of vegetation, such as chlorophyll absorption and near-infrared reflectance, which are vital for distinguishing between forest and non-forest areas. The selection of these indices is thus driven by their effectiveness in enhancing the accuracy of vegetation detection and their ability to capture key spectral characteristics of the vegetation. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Equation** & & & & **Equation Adjusted for** & & \\
**No.
## Methods
### Seismic Equipment Gaia 2T  
As reference equipment, a Gaia 2T apparatus with ViGeo2 seismic sensors was used (made by Vistec, Praha). The Gaia 2T equipment is a three-channel seismic station with a dynamic range of 138 dBp-ps and the option for triggered or continuous digital data recording. Synchronization of time was provided with a GPS module, and data were recorded on compact flash drives. ViGeo2 is a compact, active, short-period, three-component, high-speed seismometer for field and stationary applications. The seismometer includes three mechanical vibrating systems (sensors) with a frequency of 2 Hz and a frequency range of 2-200 Hz. The frequency range of the sensor is shown in Figure 6; the horizontal axis shows frequency, and the vertical shows amplitude.  
Figure 7 shows the complete assembly of a standard Gaia 2T seismic station with the ViGeo2 speed sensor made by Vistec. It includes a GPS antenna for synchronizing time and a data and power supply cable.  
Figure 5: Frequency characteristic of the interferometric sensor.  
Figure 6: Frequency range of the ViGeo2 sensor.  
The SWIP program (Seismic Waves Interpretation Program) delivered as the standard by Vistec Praha to Gaia Instruments was used to process seismic data. This program enables a seismic signal to be processed according to both time and frequency. In regard to amplitude, the processing software does not allow the values of vibration amplitudes to be recalculated into physical units (mm\(\cdot\)s\({}^{-1}\)); therefore, the vertical axes in all figures showing wave images are plotted in quantization levels (cnt). The conversion formula for the ViGeo2 sensor is as follows: \(1 cnt=2.975\times 10^{-6}\) mm\(\cdot\)s\({}^{-1}\) (more in [48,49])
## 3 Base Station Deployment and Self-Optimization Positioning Method
### Self-Optimization Positioning Method for Base Stations
#### 3.3.1 A-Type Central Area  
Taking Figure 3 as an example, the A-type central area selects base station nodes 6, 7, 10, and 11 as the basis for distance measurement information in positioning. Facing the distances d1, d2, d3, and d4 from four known coordinate base station nodes to the target mobile terminal, this paper assigns weights to each distance measurement information based on the weighting formula from Section 2, with weights denoted as p1, p2, p3, and p4.  
In the selection of distance measurement information, the first choice is the distance information with a weight greater than or equal to 0.5. However, within the square positioning area with side length \(x_{b}\), it is not possible to find three reliable high-precision distance measurements with weights greater than 0.5 at every location. Three specific scenarios are as follows:  
1. There are four distance measurements with weights greater than or equal to 0.5;
2. There are only three distance measurements with weights greater than or equal to 0.5;
3. There are only two distance measurements with weights greater than or equal to 0.5.  
Due to the division of transition areas, there will be no distance measurements in the Type A central area with a distance less than \(x_{a}\); thus, only the first and second scenarios need to be considered.  
In the first scenario, this paper adopts a weighted average approach. By selecting any three out of the four distance measurements for permutation and combination positioning, four positioning coordinates (x1, y1), (x2, y2), (x3, y3), and (x4, y4) can be obtained. Simultaneously, the weights q1, q2, q3, and q4 for the four positioning coordinates are assigned according to the following formula:  
\[q_{i}=\frac{p_{j}+p_{m}+p_{n}}{3} \tag{5}\]  
In Equation (5), \(p_{j}\), \(p_{m}\), and \(p_{n}\) represent the weights of the three distance measurements used in the calculation of the i-th coordinate.  
After obtaining the weights for each coordinate, the final positioning coordinates are determined using the weighted average formula as follows:  
\[(x_{i},\ \ y_{i})=\frac{(\sum q_{i}x_{i},\ \sum q_{i}y_{i})}{\sum q_{i}} \tag{6}\]  
For the second scenario, distance measurements with weights less than 0.5 are directly discarded, and the three distance measurements with weights greater than or equal to 0.5 are selected to calculate the two-dimensional coordinates.
## 2 Air Quality Monitoring in Terms of Particulate Matter
### Monitoring of PM--European Data
However, at majority of monitoring stations the research on PM\({}_{2.5}\) chemistry is not conducted in a continuous manner. They are usually carried out randomly (selected days of the month) or concern aggregated weekly samples. In fact, the German Melpitz station is the only institution where the investigations of the concentrations of anions, cations and carbon matter are conducted in the 24-h PM samples over the whole year [58]. Although the data on the full chemical composition of PM, measured in the main monitoring networks within the Europe (EEA, EMEP) are limited, many information is provided by research projects, carried out by scientific institutions and development units and large infrastructure projects [34]. Among the latter the following should be mentioned:  
European Supersites for Atmospheric Aerosol Research (EUSAAR) [83],  
Figure 1: PM\({}_{2.5}\) concentrations in Europe (annual mean) in 2016 [12]. Dots designate PM\({}_{2.5}\) concentrations in the following ranges [in \(\mu\)g\(\cdot\)m\({}^{-3}\)]: \(\bullet\)\(\leq\) 10; \(\circ\) 10–20; \(\bullet\) 20–25; \(\bullet\) 25–30; \(\bullet\)\(\geq\) 30; \(\square\) No data; \(\square\) Countries/regions not included in the data exchange process. Aerosols, Clouds and Trace gases Research InfraStructure Network (ACTRIS) [84],  
Integrated non-CO\({}_{2}\) Greenhouse Gas Observing System (InGOS) [85];  
Chemical On-line Composition and Source Apportionment of Fine Aerosol (COLOSSAL) [86]. Monitoring of air pollution with particulate matter in Poland has been carried out for many years as a part of the National Environmental Monitoring (NEM) subsystem [87], however so far an attention has focused mainly on the PM\({}_{10}\) fraction. The research on PM\({}_{2.5}\) has been conducted systematically since 2000, at various measurement points of the country, belonging to the RIEP (Regional Inspectorate for Environmental Protection) network and within the projects carried out in the research and development sector. As pointed out in numerous scientific publications, for example, [15; 17; 31; 32], an information on PM\({}_{2.5}\) concentration and especially on the chemical composition of PM\({}_{2.5}\) is still insufficient. There is a need to systematize knowledge on the problem of air pollution with PM\({}_{2.5}\)[15; 31]. This is especially important in Poland where relatively high concentrations of PM\({}_{2.5}\) considerably exceed the European levels. In addition, Poland is a specific country in terms of emission conditions-high share of fossil fuels in energy production together with high emissions from industry against the Western European countries [27; 32; 69].
# A Combined Anomaly and Trend Detection System for Industrial Robot Gear Condition Monitoring
## 2 Materials and Methods
### Combined Anomaly and Trend Detection Model
#### 2.1.2 System Design  
The objective of the presented system is to evaluate whether \(x_{anomaly}(t)\
eq 0\) or \(x_{trend}(t)\
eq 0\). For this, an anomaly detection model and a trend detection model are deployed in parallel. The detection of an anomaly in a defined number of sequential measurements leads to the recommendation of immediate maintenance actions. The detection of trends in the data of a defined number of a sequential measurements leads to the proposal of maintenance actions in the near future. The working principle of the system is summarized in Figure 2. The design of the system addresses different aspects of the industrial robot gear condition monitoring use case. Faults, whose manifestation but not the underlying fault mechanism progress (e.g., tracking of the growth of a crack in a gear tooth) can be tracked with HIs, will cause point or collective anomalies. The AD model will be used for the detection of such faults. Other faults, whose progress can be tracked (e.g., increasing wear), will cause trends in the HI. These trends will be detected by the trend detection model.
## 2 Materials and Methods
### Statistical Analyses  
All variables are expressed as means, standard deviations (SDs), minimum and maximum values (range), and 95% confidence intervals (95% CI). Mean differences between normal weight and overweight participants were tested using a one-factor univariate general linear model [28]. Differences between means were considered statistically significant if \(p\) < 0.05 and partial eta-squared (\(\text{n}_{\text{p}}\)\({}^{2}\)) > 0.15 [29]. A power calculation (nQuery Advisor 4.0; Statistical Solutions, Saugus, MA, USA) was performed using previous data [13], which suggested a sample size of 13 participants in each group, where the study would have 80% power to detect a mean difference of 2.90 cm in CM] performance using a two-sided \(t\)-testwith a significance level of \(p<0.05\) under the assumption of a pooled standard deviation of 2.50 cm [28].  
A two-step linear regression analysis (with \"inclusion\" as the method) was conducted to test for relationships between the anthropometric parameters (%BF, body weight, and body height) and performance parameters (e.g., sprinting, jumping, and throwing). In the first step, body weight and body height were used as predictors with the regression model. In order to evaluate the influence of %BF and the additional explained variance, %BF was included in the second step in the analysis as a third predictor. \(R^{2}\) and the change of \(r^{2}\) between steps 1 and 2 and the nonstandardized regression coefficient \(\beta\) for all predictors and steps are reported. Pearson product-moment correlation coefficients (\(r\)) were interpreted as follows: <0.1, trivial; 0.1-0.3, small; 0.3-05, moderate; 0.5-0.7, large; 0.7-0.9, very large; 0.9-1.0, almost perfect [30]. Therefore, \(r^{2}>0.5\) (explained variance \(>50\%\)) was defined as meaningful and is marked in bold. Regarding the sample size of \(N=25\), the critical value for the product-moment correlation based on a two-sided \(t\)-test and \(\alpha=5\%\) is \(r=0.381\)[31].  
The intraclass correlation coefficient (ICC) and the coefficient of variation (CV) were calculated to quantitatively assess the intrarater reliability [32]. The interpretation of the reliability values was based upon [PERSON] and [PERSON] [33], [PERSON] [30], [PERSON] [34], and [PERSON] [35]. The ICC was considered to show excellent relative reliability (intersubject variability) if it was >0.75, 0.40-0.75 was considered fair to good, and <0.40 was considered poor. The CV (the indicator of measurement variability and random error, i.e., absolute reliability) was determined after log-transforming the raw data [36]. The CV can be defined as good with values <10% [37,38]. All statistical analyses were performed using SPSS version 25.0 for Windows (SPSS Inc., IBM, Armonk, NY, USA).
# Strain Measurement Distributed on a Ground Anchor Bearing Plate by Fiber Optic OFDR Sensor
## 6 Conclusions  
In order to monitor the safety of soil slopes reinforced by ground anchors, the tensile forces of anchors should be measured. The tensile force of a ground anchor can be determined by measuring the strain of a bearing plate that transfers the tensile force of the anchor to the ground, because the strain can be assumed to be proportional to the ground anchor force. Therefore, the strain distribution of the bearing plate was investigated by a fiber optic OFDR sensor, which was fabricated from a tunable laser source, an auxiliary interferometer and a main interferometer. This OFDR sensor was operated through a sweep range of 500 GHz with a spatial resolution of 0.2 mm, and a strain accuracy of approximately 4 \(\mu\)e, considering the system noise when processing the signal in 5-cm segments. The sensing fiber was circularly bonded onto the bearing plate using epoxy, and the distributed strain was measured on the bearing plate while increasing the load up to 10 t. As a result, it was confirmed that the bearing plate was subjected to not only compressive deformation but also bending deformation. It was considered that the complicated strain distribution of such a bearing plate can be seen in reality. Therefore, the deformation behavior of the bearing plate should be carefully considered when measuring the strain of the bearing plate in order to monitor the anchor load. That is, when the strain distribution of the bearing plate does not have a point symmetric distribution due to the anchor load, it can be understood that it is necessary to carefully study the calibration to monitor the load of the anchor by measuring one point of strain of the bearing plate. However, in future, by measuring the circumferential distribution strain of the anchor bearing plate, the tensile force of the anchor may be able to be monitored by analyzing the bearing plate deformation behavior.  
[PERSON] developed the concepts, designed the experimental methodology, analyzed the data, wrote, reviewed and approved the final paper. [PERSON] and [PERSON] conducted the experiments, analyzed the data, wrote and edited the paper. [PERSON] and [PERSON] wrote and edited the paper.  
This research was supported by \"Development of safety measurement technology for cable systems of major infrastructures\", funded by the Korea Research Institute of Standards and Science (KRISS-2018-18011073).  
The authors would like to thank [PERSON], and [PERSON] (Korea Institute of Construction Technology) for sharing the knowledge about anchors and soil slope monitoring.  
The authors declare no conflict of interest.
## 7 Conclusions  
This paper tries to enhance network peak load regulating capacity and to increase system efficiency by adding an extra portable energy resource system and an extra heat storage tank to the distributed network.  
After installing an extra portable energy resource system to the distributed microgrid, the amount of fossil fuel energy consumption is reduced by about 8%. Additionally, by installing an extra portable energy system, the distributed network can obtain additional benefits by exporting electricity to the main grid at the peak demand time, which leads to a 28.29% reduction of system daily operational cost. More importantly, with the installation of the portable energy system, the peak load regulating capacity has been significantly improved, which can be reflected by the fact that much more electrical energy can be sold back to the main grid at the peak demand time, and the portable energy system has greater potential to supply electrical power for the distributed grid.  
However, after introducing an extra portable energy resource system to the distributed system, the system's uncertainty can be increased to some extent. In this paper, chance constrained programming is proposed to deal with system uncertainty caused by the added extra portable energy system. By applying the PSO-MC algorithm to optimize the chance constrained programming problem in the proposed network, simulation results show that power system economy and system uncertainty can be compromised by selecting appropriate confidence levels \(\alpha\) and \(\beta\). In addition, compared with existing optimization algorithms, the proposed PSO-MC has the obvious advantages of higher computational efficiency, fewer stochastic variables, and higher accuracy.  
When introducing an extra heat storage tank to the distributed microgrid, system thermal outputs and electrical outputs can be decoupled. After decoupling heat and power, thermal demand can be supplied by the heat storage tank independently if necessary. Alternatively, the CHP unit can work at its rated power to simultaneously generate heat and power at the peak demand time, and the redundant heat can be stored in the heat storage tank, which promotes CHP output efficiency and reduces fossil fuel energy consumption as well. Moreover, simulation results show that battery installation capacity can be reduced by about 40% and system reliability can be improved if an extra heat storage tank is installed.  
Figure 8: Total electrical energy needs to be stored in the battery.  
**Acknowledgments:** The authors gratefully acknowledge the financial support from the project of \"Research on the operation simulation and effectiveness evaluation technology of power market (YDB17201600102)\".  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] analyzed the data; [PERSON] revised the paper; [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.