esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Result and Discussion  
### Porosity  
Porosity is the ratio of total pore volume to sample volume; it represents the percentage of pore volume in the sample. The porosity of each sandstone sample is obtained from a graph, such as in Figure 2, as follows. Figure 2 is a result of sandstone samples from the NMR test. In Figure 2, T\({}_{2}\) is linearly related to pore radii, therefore, the T\({}_{2}\) spectrum curve represents the pore radii distribution of samples. The cumulative porosity curve is the integration of pores volume; therefore, the peak value of it represents the porosity of samples.  
Figure 3a displays the porosity variation of sandstone after different F-T cycles. Figure 3a shows that the porosity of sandstone gains evident growth with F-T cycle increase. This phenomenon occurs in all acid solutions. Take samples immersed in acid solution with pH = 2.8 as an example, when the F-T cycle is 0, the porosity is 10.02%, then the porosity reaches 13.67%, 15.17%, 16.90% and 18.42%  
Figure 1: A plot of \(\lg(S_{V})\) versus \(\lg T_{2}\) of sandstone, the slope of \(\lg(S_{V})\) versus \(\lg T_{2}\) represents the value of 3-D; D\({}_{1}\) represents fractal dimension of micro pores, D\({}_{2}\) represents fractal dimension of meso pores and D\({}_{3}\) represents fractal dimension of macro pores.  
after 10, 20, 30 and 40 F-T cycles, respectively, corresponding to porosity growth rates of 36.43%, 51.37%, 68.70% and 83.83%. It can be found that the porosity grows more remarkably during 10 F-T cycles. This is mainly because the acid solution is more concentrated during 10 F-T cycles. The soluble component of sandstone, including Illite, Montmorillonite and Ferric components, dissolved faster in this period. With an F-T cycle increase, the concentration of the acid solution decreased and porosity growth rate slowed down relatively.  
In terms of the effect of the acid solution on porosity growth, it has not shown a clear and consistent variation trend in the whole F-T treatment process, while the porosity growth was obviously affected by the acid solution during the 10 F-T cycles. As can be seen from Figure 3b, it records the pH of various acid solutions during the experiment and reveals that acid erosion mainly worked during the first 10 freeze-thaw cycles, as pH is exponentially related to the concentration of acid solution. As seen from Figure 3c, with the pH value goes down, the increase in porosity goes up; specifically, the porosity growth of sandstone immersed in acid solution with pH equals 2.8, 4.2, 5.6 and 7.0 under 10 F-T cycles that are 3.65%, 3.36%, 3.33% and 3.20%, respectively. Therefore, the result indicates that the coupled effect of F-T weathering and acid erosion obviously degrade the sandstone, and F-T weathering is more effective than acid erosion in the degradation.  
Figure 3: _Cont._  
Figure 2: T\({}_{2}\) distribution and cumulative porosity curve of sandstone.
# Generalized Replay Spoofing Countermeasure Based on Combining Local Subclassification Models
## 3 Related Works  
Audio classification seeks learning a model that is able to predict the category of unknown audio utterance [26]. This machine learning task can benefit many practical fields such as medical applications related to diagnosing sleep bruxism [27], dementia [28], and depression [29]. Moreover, industrial applications exploited audio classification techniques for several scenarios, such as detecting machine chatter [30] and the condition of rotating machines [31] Furthermore, environmental sound recognition [32; 33] has contributed to the understanding of the context of the occurring audio. In fact, it is crucial to trigger decisive actions such as evacuating a building when an alarm occurs or reaching a baby when he cries.  
Recently, the ASVspoof challenge series ([[https://www.asvspoof.org](https://www.asvspoof.org)]([https://www.asvspoof.org](https://www.asvspoof.org)), accessed on 10 October 2022) deployed antispoofing benchmarks [34; 35; 36]. This triggered the research on presentation attack detection [2], particularly speech synthesis (SS), voice conversion (VC), and replay attacks.  
Typically, state-of-the-art approaches consist of classifying a voice utterance as genuine or spoofed. These approaches are based on conventional classification paradigms, deep learning paradigms, or a combination of supervised and unsupervised learning paradigms.
# Microscope-Based Optical Frequency Comb Generator for 200 GHz Spaced WDM Data Transmission System
## 3 Results
### Architecture and Simulation of 4-Channel 200 GHz Spaced IM/DD WDM-PON Transmission System
The drop in the BER performance is mainly affected by the power and noise floor variation between comb lines and phase noise. In such a case, the comb source for the data transmission system  
Figure 11: Quality of transmission (QoT) characteristics for the 10 Gbit/s NRZ-OOK signals in the 4-channel 200 GHz spaced IM/DD WDM-PON system: the fair comparison of BER vs. Optical fiber length for implemented microsphere-based OFC comb carriers’ performance. Figure 10: Optical spectra: (**a**) after user-defined OBPF with implemented microsphere-based OFC comb source, (**b**) after OBPF-four optical carriers from microsphere-based OFC comb source, (**c**) modulated optical carriers after B2B transmission for 4-channel 200 GHz spaced IM/DD WDM-PON system operating at 10 Gbit/s per channel. must ensure minimal optical carrier-to-noise power ratio (OCNR) that a comb line must have to be useful for data transmission. We have demonstrated 4-channel 200 GHz spaced IM/DD WDM-PON transmission system with operating data rates of 10 Gbit/s per channel over different SMF fiber link section lengths up to 60 km, please see Figure 12. As shown in Figure 12a-c, for the 1 st optical channel with worst BER performance in B2B configuration, as well after 20 and 40 km, the signal quality is very good, the eye is open, and error-free transmission can be provided. After 60 km transmission, the BER of the received signal was \(9.1\times 10^{-4}\), please see Figure 12d. Therefore, our investigated 200 GHz spaced OFC-WGMR light source-based IM/DD WDM-PON transmission system is fully capable of providing 10 Gbit/s of NRZ-OOK modulated signal transmission according to NG-PON2 recommendation specified valid optical link distances of 40 km. That means it is technically challenging to ensure such transmission stability for longer distances by the use of OFC-WGMR as an optical light source for telecommunication applications. More comprehensive future research on the limits of the OFCs parameters for implementing the PON transmission systems segment of such a long transmission distance (60 km) is desirable.
## 3 Dynamic Weight Modification Procedure
### Applicability of the Proposed Scenarios  
In this subsection, we briefly discuss the reasons why we choose the listed scenarios. The two simplest link weight modification scenarios are discrete and linear. The discrete scenario for a given range of link utilization returns a constant link weight, while the linear one represents a continuous range of weight values. In the case of the linear scenario, various slopes of the linear function indicate the load increase rate. The choice of these scenarios was dictated by their simplicity, and we believe that it was worth comparing them with more advanced scenarios.  
The M/M/1 model is a good description of the situation where packet arrivals are random and service time is relatively unpredictable. The M/M/1 model provides a valuable foundation for understanding basic packet queuing behavior in the network. However, the link weight should reflect the operation of various services and not all of them may behave in accordance with the M/M/1 model.  
The M/G/1 model is more flexible as it can handle various service time distributions of packets. It is useful when exponential service times are not a good assumption. This model better describes diverse services, especially when considering flow durations. This is important from the perspective of maintaining individual flows in flow tables. In our case, this issue is relevant to DFT tables.
## 4 Results and Discussion
### Time-Domain Electromagnetic (TDEM) Method
The interpretation after the inversion of the data is also included: the red straight lines represent the resistivity as a function of depth. P: Palaeozoic phyllites; T: Triassic; M1: rudites and calcarenites from the basal Miocene unit (upper aquifer); M2: marks from the upper Miocene unit. Figure 5: Stratigraphic columns deduced from the TDEM method information at each of the measurement stations (1, 2, 3, 4, 5 and 6). The numbering in black corresponds to the depths at which the reference levels were found (top of the basal Late Miocene calcarenite/conglomerate and base of the Triassic). The numbering in red corresponds to the elevation of the land surface at the point where the TDEM measurement was taken and to that of the two reference levels. a: folded phyllities; b: breccias or conglomerates; c: sandstones; d: lutites; e: marks and marly limestones. At Stations 4, 5 and 6, the contact between assemblies M2 and M1 is characterized by a significant increase in resistivity associated with the top of the Miocene calcarenites, breccias or polygenic conglomerates. This contact was found at depths of 133 m, 139 m and 126 m at Stations 4, 5 and 6, respectively (Figure 4). If the values between Stations 1, 2 and 3 and Stations 4, 5 and 6 are compared, a vertical movement of approximately 50 m is obtained, which is consistent with the information provided by hydrogeological boreholes and the seismic profile. The contact of the sedimentary cover with the Palaeozoic basement is also associated, in this case, with a rise in resistivities at depths of 252 m, 253 m and 252 m (at the different stations shown in Figure 4). If the elevation of the terrain at each station and the depth measurements are considered, the elevations above sea level of this contact would be 118 m, 123 m and 115 m at Stations 4, 5 and 6, respectively (Figure 5). The elevation obtained for this contact using this geophysical technique is very similar to that derived from direct data in borehole 2 (100 m). Additionally, if the elevation data of the basement at the six stations are compared, the presence of fault 2 is also deduced by means of the TDEM method. In Figure 5, the lithological columns deduced for each of these TDEM stations are correlated. They indicate the depth measurements up to the reference levels (in black), as well as the elevation of these surfaces above sea level (in red). If the TDEM X-X\({}^{\prime}\) profile (Figure 5) and the seismic profile of Figure 3 are compared, it can be noticed that fault 2 is detected by both techniques. Also, in both cases, another structure (Fault? in Figures 3 and 5) is inferred, which is characterized by a deepening of the Miocene base (M1) that does not affect the height of the Palaeozoic basement.
## 6 Conclusions and Future Work  
This study has investigated the performance of fake source packet routing and phantom node routing protocols. The protocols are used for source location privacy (SLP) protection in monitoring WSNs. Fake source packet routing protocols have demonstrated some limitations including exhaustive energy consumption, low packet delivery ratio (PDR) and long end-to-end delay (EED). To address the limitations, this study has proposed two new phantom-based routing protocols, the PhaP and PhaT protocols. Based on strategies of two existing fake source packet routing protocols, the proposed protocols introduce new two-level phantom routing techniques. The routing strategies in the PhaP and PhaT protocols ensure two adversary confusion phases. Packets are routed from the source node to the destination sink node through the first adversary confusion phase and then through the second adversary confusion phase. In the PhaP protocol, the second level adversary confusion phase is executed inside a pursue ring located at some distance away from the sink node and a directed random-walk routing strategy is employed. In the PhaT protocol, the second level adversary confusion phase is executed inside a region near the network border and a backbone route is employed. Analysis results show that the proposed PhaP and PhaT protocols are capable of achieving strong SLP protection.  
Furthermore, by eliminating the fake packet traffic in the network, the protocols demonstrate superior performance features compared to the fake source packet routing protocols. The PhaT protocol preserves strong SLP protection with improved communication overhead than its contender TreeR protocol. Equally, the PhaP protocol preserves stronger SLP protection than its contender ProbR protocol with controlled communication overhead. Moreover, the experimental analysis reveal that the proposed protocols show practical results under varied network configurations. The proposed protocols can be practical in monitoring systems which guarantee strong SLP protection with strict requirements on energy efficiency. As part of future work, approaches to reduce the complexity of the protocols and techniques to improve reliability will be explored. Furthermore, the influence of the routing protocols on the network lifetime performance will be investigated.  
Conceptualization, L.C.M.; formal analysis, L.C.M.; funding acquisition, S.S.; investigation, L.C.M. and S.S.; methodology, L.C.M.; supervision, S.S.; writing--original draft, L.C.M.; writing--review and editing, S.S. All authors have read and agreed to the published version of the manuscript.  
This research was partly supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education with Grant No. NRF-2018R1D1A1B07048338 and a part of the project titled \"Development of Automatic Identification Monitoring System for Fishing Gears\", funded by the Ministry of Oceans and Fisheries, Korea with Grant No. 20170388.  
The authors declare no conflict of interest.
# Around View Monitoring-Based Vacant Parking Space Detection and Analysis
## 3 Algorithm to Detect Vacant Parking Spaces
### Space Occupancy Classification Stage
#### 3.3.2 Naive Bayes Classifier
For the space occupancy classification stage, two perspectives were used to detect features within the parking space, which can be classified as road or obstacle features. For road features, a region growing method was used to search for usable space within the parking space. For obstacle features, an edge detection method was used to determine whether a car is parked in the parking space. Finally, features were output to an NB classifier to classify the current status of the parking space.
## 3 Results
### Validation and Evaluation  
Table 6 and Figure 10 show the validation indexes of SOC prediction by the five models. ANN exhibited the lowest RMSE of prediction (RMSEP) of 3.718 and the highest coefficient of determination of prediction (R\({}^{2}\)P) of 0.391. This approach was the optimal method to predict SOC in the study area. The suboptimal prediction model was OK, with a slightly higher R\({}^{2}\)P (0.372) and a lower RMSEP (3.727) than the other models, followed by SVM with RMSEP and R\({}^{2}\)P of 3.753 and 0.361, respectively. Furthermore, PLSR demonstrated a better result than SLR with higher R\({}^{2}\)P (0.283) but unexpectedly showed the highest RMSEP (4.087). The SLR model did not effectively perform with the lowest R\({}^{2}\)P (0.281) and highest RMSEP (3.930). These results demonstrated that the NDVI time series data had high correlation with SOC and can be successfully used to predict SOC contents as good indicators of the primary ecological productivity of soil vegetation. 0 The predicted SOC values were approximately close to the measured ones to some extent. Figure 10 illustrates that the predicted value obtained by the ANN method was close to the measured value (1:1 red line). The ANN model reflected high SOC values, whereas other methods suppressed high values and highlighted low values. The scatter plot showed that the predictive ability of the SVM method for high-value part was only  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **X-Block** & **X-Block (NDVI)** & **Y-Block (SOC)** & **X-Block (NDVI)** \\  & **(NDVI) LVs** & **Cumulative** & **LVs** & **Cumulative** \\ \hline
**1** & **93.69** & **93.69** & **55.48** & 55.48 \\ \hline
2 & 1.52 & 95.22 & 10.68 & 66.16 \\
3 & 1.45 & 96.67 & 1.71 & 67.87 \\ \hline \hline \end{tabular} Note: LVs: latent variables (or principal component); RMSECV: root mean square error of cross-validation; NDVI: normalized difference vegetation index; SOC: soil organic carbon.  
\end{table}
Table 4: Predicted results of SOC via the PLSR model (percent variance captured by the regression model).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number Nodes** & **RMSEcal SOC** & **RMSEcv SOC** \\ \hline
1 & 4.67 & 4.89 \\
2 & 4.39 & 4.84 \\
3 & 4.22 & 4.81 \\
4 & 4.33 & 4.79 \\ \hline \hline \end{tabular} Notes: RMSEcal: root mean square error of calibration; RMSEcv: root mean square error of cross-validation; SOC: soil organic carbon.  
\end{table}
Table 5: Number of nodes in the hidden layer and corresponding precision in the ANN model.  
second to ANN. The scatter plots of PLSR and SLR methods were generally similar, whereas the OK model exaggerated the low value of SOC. On the whole, these findings indicated that ANN was the optimal model for SOC prediction. These findings were supported by [PERSON] et al. (2014) and [PERSON] et al. (2019), who concluded that the ANN model shows the best performance for SOC prediction, followed by the SVM model. The proposed ANN models were more feasible than the other methods in extracting existing patterns among the ancillary data and SOC.
## 3 The Proposed Multi-Scale Demodulation Method
### Fault Diagnosis Procedure Based on the Proposed Method  
The detailed fault diagnosis procedure based on the proposed method is shown in Figure 7. The raw vibration signal is first acquired from the machinery. Then, the EMD method is applied to decompose the raw vibration signal into \(n\) IMFs, denoted as \(c_{1}\), \(c_{2}\),..., \(c_{n}\). Different IMFs have different sensitivities to fault-related components. In order to choose the most fault-related IMF and suppress the fault-unrelated IMFs, a weighted-EMD de-noising technique is presented based on [PERSON]'s correlation analysis and a weighting method.  
First, the correlation coefficients between the raw vibration signal and different IMFs are calculated [32; 33],  
\[\rho_{i}=\frac{\sum_{k=1}^{K}[x(k)-\bar{x}][c_{i}(k)-\bar{c}_{i}]}{\sqrt{\sum_{ k=1}^{K}[x(k)-\bar{x}]^{2}\sqrt{\sum_{k=1}^{K}[c_{i}(k)-\bar{c}_{i}]^{2}}}},\qquad i =1,2,\ldots,n \tag{8}\]  
where \(x\) refers to the raw vibration signal, \(\bar{x}\) is the mean of \(x\), \(K\) equals the length of \(x\), \(c_{i}\) stands for the \(i\)th IMF and \(\bar{c}_{i}\) is the mean of \(c_{i}\). Then, IMFs are partitioned into noise-free IMFs and noise-dominant IMFs with a given threshold.  
The reconstructed signal can be obtained from the noise-free and noise-dominant IMFs after a weighting process.  
\[x_{rec}=\sum_{i=1}^{n}w_{i}xc_{i} \tag{9}\]  
where \(w_{i}\) refers to the weight of the \(i\)th IMF.  
\[w_{i}=\begin{cases}\rho_{i}/max(\rho),&\rho_{i}\geq\rho_{m}\\ 0,&\rho_{i}\leq\rho_{m}\end{cases} \tag{10}\]  
Figure 6: Waveform of the Morlet wavelet.  
Figure 7: Fault diagnosis procedure based on the proposed method.  
where \(\rho=[\rho_{1},\rho_{2},\cdots,\rho_{n}]\) denotes the correlation coefficient vector of \(n\) IMFs. \(\rho_{m}\) indicates the given threshold for IMF grouping; in this paper, it is chosen as the mean value of \(\rho_{m}\) as an example to study the effectiveness of the proposed method.  
After that, CWT is carried out to obtain the TFR \(W_{rec}(t,f)\) of the reconstructed signal. Then, the time-frequency impulse envelope signal \(y_{im}\) can be calculated from the \(W_{rec}(t,f)\) through a summation along the frequency direction. Furthermore, autocorrelation is performed to \(y_{im}\) to improve the signal-to-noise ratio. Finally, the time-frequency impulse envelope spectrum can be obtained for fault diagnosis after fast Fourier transformation.  
\[y_{im}(t)=\sum_{f=0}^{f_{c}}W_{rec}(t,f),\qquad f_{c}=f_{s}/2 \tag{11}\]  
\[\hat{y}_{im}(l)=\sum_{k\in Z}y_{im}(k)y_{im}(k-l),\qquad k=1,2,\cdots,K \tag{12}\]  
\[Y_{w}=\int_{-\infty}^{\infty}\hat{y}_{im}(t)e^{-iwt}dt \tag{13}\]  
where \(f_{s}\) refers to the sampling frequency, \(f_{c}\) is the Nyquist frequency and \(l\) means the time lag.  
Above all, the fault-related components can be demodulated according to the proposed method, and there is no need to select optimal sub-band signals for different rotating speed conditions.  
Validation of the Proposed Method by the Fault Diagnosis of a Planetary Gearbox with a Sun Gear Spalling Fault
# A Survey on the Application of WirelessHART for Industrial Process Monitoring and Control
## 4 Challenges and Design Requirements
### Cost Effectiveness and Resource Utilization  
The prime motivation for transitioning from wired to wireless solutions is the low cost requirements for deploying and installing wireless field devices. IWSN is intended to provide increased productivity, reduced maintenance, and decreased operating costs [112]. Some wireless sensor solutions cost less than $200 for deployment at the field level, while the same wired device installation costs can be doubled because of additional laying and wiring costs [113]. Most WirelessHART field devices are compact and small in size, which is an added advantage when encountering factory space problems, helping to make installing a large-scale network of nodes easier [114]. However, this compactness reduces the computational capabilities because of the limited memory and battery power supply. This situation also causes the WHNCS to suffer from a limited operational range in harsh industrial environments, which makes real-time data delivery challenging [115].
## 3 Results
### MnTnHex Enhances Cisplatin-Induced Cell Death  
The induction of cell death and the cell cycle progression was analyzed by flow cytometry (Figure 5). The cellular DNA content assessment showed that MnTnHex per se increased the percentage of the sub-G1 population in both cell lines at 1 uM. This was also observed at 0.5 uM but only for H1975 cells. Importantly, a strong increase in the percentage of the sub-G1 population was found when cisplatin was added in combination with MnTnHex (Figure 5D,G) in both cell lines. In terms of magnitude, the combination reached higher % values of sub-G1 cells in the H1975 cell line (~22%) when compared with A549 cells (~16%). Nevertheless, this effect was evident for both concentrations of MnTnHex in A549 cells (0.5 and 1 uM; Figure 5D), while for H1975 cells, significant results were only achieved with 1 uM (Figure 5G).  
Figure 5: Effect of MnTnHex combined with cisplatin in A549 and H1975 cells on cell cycle distribution. Cells were treated with the indicated concentrations of MnTnHex and cisplatin (1 uM) for 72 h and DNA content was assessed on fixed cells by flow cytometry. (**A**) Representative flow cytometry histograms of PI-stained cells. Summary data of cell cycle populations for (**B**) A549 and (**C**) H1975 cells. Individual populations for (**D–F**) A549 and (**G–I**) H1975 cells. Values represent mean \(\pm\) SD (\(n\) = 3–4) and are expressed as percentages of total cells. * \(p\) < 0.05, ** \(p\) < 0.01, *** \(p\) < 0.001 and *** \(p\) < 0.0001 (one-way ANOVA with [PERSON]’s multiple comparisons test) when compared with control cells, or between any two conditions as indicated in the figure by connecting lines.  
For A549 cells, a mild G2/M arrest accompanied by a reduction in G0/G1 was observed when cisplatin and MnTnHex were added in combination, while the individual treatment with each of these compounds did not have a significant effect (Figure 5E,F). Overall, these results suggest that the observed reduction in cell viability present in the combinatory study mentioned in Section 3.3 and depicted in Figure 4 could be ascribed to the induction of cell death.