esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology for Dynamic Risk Evaluation and Early Warning of Crest Cracking
### Risk Estimation and Early Warning of the Dam Crest Cracking
The mean values of the probabilities at the observed cracking times are around 50% and the 5% and 95% quantiles of the probabilities are approximately 25% and 80%, respectively. Figure 14: The mean values and confidence intervals of the calculated probabilities at the cracking times for all the past cracking events. Figure 13: The estimated mean value and confidence interval of cracking probability based on the distribution of \(\lambda\) updated with the information on last crest cracking event, as well as the observed next cracking time and the corresponding computed mean value of cracking probability. For verifying the ability of the proposed methodology in early warning of crest cracking, the cracking times for the observed crest cracking events (the first crest cracking event is excluded) are estimated by Equation (4) using the mean values and the 5% and 95% quantiles of cracking probabilities in Figure 14 with the progressively updated mean value of \(\lambda\) (see Table 5). The estimated mean values (corresponding to the average cracking probabilities) and upper and lower limits (corresponding to the 95% and 5% quantiles of cracking probabilities) of cracking times are shown in Figure 15, where the origin of the time axis (ordinate) corresponds to the beginning of abnormal state, and are compared with the actual cracking times. It is observed that the actual cracking times are between the upper and lower limits of estimated cracking times. The widths of ranges of estimated cracking times are reduced as the mean value of \(\lambda\) decreases with accumulation of the observed data. However, due to limited observed information and the significantly random nature of the studied problem, the ranges of estimated cracking times are fairly broad. Nevertheless, for the specific dam and the observed crest cracking events, the actual cracking times are essentially located between the mean values and lower limits of estimated cracking times, with which the possible occurrence times for future crest cracking events would be predicted. In dam safety management, it is required to early warn crest cracking event, so that preparation for remediation measures can be made in advance. The early warning of crest cracking could be realized by estimating the lower limit of cracking time using the proposed methodology. Figure 16 shows the early warning times (i.e., the period between the actual cracking time and the estimated lower limit of cracking time) for the crest cracking events. It can be seen that for the specific dam, early warning of crest cracking can be issued from 20 days to 70 days ahead of the occurrence of crest cracking event. Figure 15: The estimated mean values and upper and lower limits of cracking times for the actual cracking events.
## 3 Results and Discussion
### Analysis of Adulterated Milk and the Influence of Temperature  
Like any other method or technique of food analysis, bioimpedance can vary according to the conditions under which it is applied, and standardisation of sample conditions is necessary to obtain reliable results. Several factors can interfere with the response, such as type of food, ion concentration, hydrophobic and hydrophilic properties, pH, temperature, and others, i.e., whenever the electrical conditions of the system are modified. As observed in Figure 8, water content directly influences the bioimpedance values. In Figure 8A,B the frequency response of the milk sample at 16 \({}^{\circ}\)C is presented. Figure 6: (**A**) Experimental setup used to evaluate the equipotential lines. (**B**) Detailed view of the copper electrode. Figure 7: Representation of the equipotential lines in \(2\,\mathrm{mol}\cdot\mathrm{L}^{-1}\) saline solution at a frequency of \(10^{6}\) Hz and concentrations of (**A**) 100% and (**B**) 12.5%; the black points indicate the measurement electrode locations and the rectangles at the opposite borders are the current injection electrodes. If one sets a working frequency, the impedance magnitude is highly affected by the water dilution. However, at frequencies greater than 100 kHz we notice a sharp drop, mainly at higher dilution percentages. At lower dilution percentages the impedance magnitude is almost flat with the frequency. Figure 8C presents such a response from other perspective. Thus, for fixed-frequency testing, the variations in impedance magnitude can be used to estimate the water content in a sample. Alternatively, if one applies the frequency response, unadulterated milk shows a flat frequency response trend. The impedance phase response shown in Figure 8B,D can also indicate water content in milk, especially at frequencies greater than 100 kHz, where higher phase variations have been found. Table 1 summarises the complex impedance measurements in both unadulterated and milk samples adulterated with distilled water. The sample temperature was 16 \({}^{\circ}\)C.  
Figure 9 presents the results for the complex impedance measurements in milk samples at 28 \({}^{\circ}\)C. The phase results in Figure 9B,D are quite similar to the measurements presented
## 5 Discussion  
This paper presents the results obtained by embedding a compact and low-power eNose in an indoor mobile robot performing package delivery. The mobile robot was assessed for the early detection of Ethanol gas leaks inside a building.  
The use of an eNose instead of a selective gas sensor to detect a gas leak has the advantage that it can be trained to detect either specific gases or odors composed of various volatile chemical compounds. This means that the detected gas or gases can be changed depending on the needs of the application or activities deployed in the building, without modifying the actual sensing element.  
The eNose used in this work is composed of sixteen single-type (identical) BME680 miniature gas sensors. In previous works, this eNose implementation has proved useful in discriminating different gases [62] and aromas [69]. This eNose is powered through a standard USB 2.0 connection and has a low power consumption of 900 mW, allowing continuous usage in battery-operated applications such as mobile robotics.  
The indoor delivery robot used in this work is an adaptation of a mobile robot initially designed to assist humans. The robot has two figurative arms able to gesticulate and includes a transportation box to operate as an indoor mobile delivery robot. More details of the robot's adaptation and the delivery operation procedures can be found in [68].  
The objective of this work was the evaluation of the delivery robot embedded with a compact eNose for the early detection of an Ethanol gas leak. The calibration procedure performed in this work consisted of exposing the eNose to Air and two Ethanol gas leaks, with evaporation rates of 4.3 mg/min and 130 mg/min. The assumption is that these two evaporation rates are the minimum and the maximum that the mobile robot can be exposed to during a package delivery operation. Then, these measurements were used to train an LDA classifier to detect Air or Ethanol.  
The indoor mobile delivery robot embedded with the eNose has been programmed to perform parcel delivery in the building. The delivery procedure described in this work has been limited to a single-floor trajectory to focus attention on the early detection of gas leaks. The robot was sent on a parcel delivery where it must enter a room with an Ethanol gas leak. The results of the real-time LDA classification showed that the compact eNose embedded in the mobile robot is capable of detecting gas leaks while making a package delivery inside a building.
## 3 Results
### Classification Performance with Feature Window Variation  
For feature window variation, we varied the feature window size from 24 h to 48 h before onset \(T_{FW}=[24,48]\), with a time gap of 24 h \(T_{TP}=24\). The feature window variation provided insights, in terms of clinical practice, about how much data is required for the best prediction performance and how different features react differently to different time windows. The model performance in the feature variation task is shown in Figure 7. A comparison of 24 h and 48 h feature windows is shown in Tables 11 and 12.  
The performance of \(V_{merge}\) was the highest among all the other features, such as \(V_{mean}\), \(V_{variance}\), and \(V_{entropy}\). Both the AUROC accuracy and NRI of \(V_{merge}\) consistently improved with increasing feature data availability. The longer the feature window, the more data was available for the model to learn from. Model performance with \(V_{entropy}\) outperformed \(V_{mean}\) when the feature window size was bigger than 28 h. The overall model performance of \(V_{entropy}\) improved drastically with more data availability, as shown in Figure 7.  
In terms of accuracy, we see a merging trend in performance of the proposed \(V_{entropy}\) feature. We can reasonably conclude that with fewer features available, our proposed entropy-based feature \(V_{entropy}\) performed better. While there the accuracy was steady in \(V_{mean}\), and there is no evidence that more data in \(V_{variance}\) would make the prediction better.  
Figure 7: Visualization plot for feature window variation performance, with the feature window size varying from 24 h to 48 h before onset, with a time gap of 24 h.  
In terms of NRI, \(V_{entropy}\) was the highest among other features after 32 h. This implied that the increase in feature window had a high impact on \(V_{entropy}\) model performance, such that the \(V_{variance}\) performance was more sensitive to the length of feature window and data availability. The model performance of \(V_{entropy}\) was sensitive to the feature window size of 24 h to 36 and 38 to 48 h of feature window size. \(V_{mean}\) was not sensitive to feature window variance. Similar behavior was found in \(V_{variance}\), the amount of available data was critical to \(V_{variance}\) between 24 to 30 h of feature window size In comparison, despite the fact that \(V_{entropy}\) performed well, in terms of accuracy, we drew the same conclusion as for AUROC; that is, \(V_{entropy}\) improved drastically with more data and a longer feature window. The NIR showed that \(V_{entropy}\) is steadily sensitive to data availability.  
In Tables 11 and 12 we showed an improvement comparison between feature window size of 24 h \(T_{FW}=24\) and 48 h \(T_{FW}=48\). The \(V_{entropy}\) feature had the highest performance improvement in AUROC, NRI, Recall and Precision. At the same time, more data may not improve the performance of \(V_{mean}\). Compared to \(V_{variance}\), more data available improves \(V_{merge}\)'s performance across all metrics.
# GIS-Based Optimal Route Selection of Submarine Cables Considering Potential Seismic Fault Zones
## 2 Materials and Methods  
The newly-developed smart decision-support tool has been built in the ArcGIS Desktop GIS computational platform, utilizing the ArcMap GIS software [44]. Nevertheless, other ArcGIS software, such as ArcGIS Pro, could also be used with the same effectiveness. In its present version, the GIS-based LCPA technique has been automatically combined with the AHP methodology and (semi-) automatically with the finite element models that have been developed by the authors via suitable scripts that have been developed utilizing Python programming language. More specifically, the optimal route of an offshore cable is selected, taking into account, among others, the quantitative assessment of the structural behavior of the cable subjected to the seismic geohazard of tectonic faulting.  
Initially, all available geodata related to the topography, geology, bathymetry, seismicity and geotectonics of the examined area were collected and stored in an extensive geodatabase within the GIS environment, as separate thematic layers. Following suitable data processing, each thematic layer was converted into a basic criterion for the AHP method and subsequently, for the LCPA technique. Having defined the basic criteria, AHP was applied and the qualitative prioritization of the criteria was transformed into weight values. Then, the GIS-based LCPA technique was applied, and several length- and cost-minimized cable routes were derived, corresponding to the different user-defined scenarios.  
Whenever the derived cable routings crossed seismically active fault zones, numerical analyses were performed to quantitatively assess the problem of fault-cable intersection, by assessing the structural distress of the cable in terms of strains. Finally, provided that the strains of the cable were below the acceptable limits, the optimal route was selected, else the process was repeated to derive alternative routings. Figure 1 depicts the main steps of the current version of the smart tool, whereas a detailed description of each step is provided in the sequence.
## 5 Experimental Testing and Analysis  
To evaluate the performance of DRSTASA, a total of 23 standard benchmark functions were selected for experimentation. The results were compared with those of nine other recent algorithms, including STA, STASA, AOA, SCA, AHA, FDA, AVOA, GWO, and the improved ASTASA, to analyze the testing performance of each algorithm. The experiments were conducted on a standard PC running Windows 10 64-bit, equipped with a 12 th Gen Intel(r) Core(tm) i5-12490F processor operating at 3.00 GHz and 16 GB of RAM. The implementation was based on MATLAB 2020a. For all algorithms, the population size and search intensity were set to 50, with parameter configurations either consistent with the original publications or based on classical settings, as detailed in Table 1.  
Figure 3: DRSTASA algorithm flowchart.
## 4 Case Study
### Simulation Process  
To evaluate the performance of the road network after a disaster, the SUMO 1.19.0 software is used to simulate and generate post-disaster travel times for each road segment, while the ArcGIS 10.8.2 software processes the road network data. The main simulation program is implemented using Python 3.8.8. The workflow for the dynamic resilience assessment of the road network under disaster conditions is illustrated in Figure 14.  
Step 1: Road network initialization. Construct a two-layer topological structure for the road network.  
Step 2: Loading initial traffic data. Obtain daily average traffic volumes for each road segment by analyzing traffic flow statistics from Tibet's transportation data sources.  
Step 3: Loading disaster event samples. Simulate five types of geohazard events, including rockfalls, landslides, debris flows, collapses, and water damage.  
Step 4: Road network performance calculation at time \(t\). Compute road segment performance using Equations (1)-(9).  
Step 5: Road network recovery during \(t\). Estimate the repair time required for all damaged infrastructure components based on Table 3, reflecting the clearance process under geohazard conditions.  
Step 6: Post-recovery road network performance calculation. Recalculate the network performance function after repairs.  
Step 7: Simulation termination condition. If the simulation period T has not yet been exceeded, proceed to the next disaster event cycle; otherwise, terminate the simulation.  
For the simulation scenario in this study, the relevant parameters and assumptions are set as follows:  
With reference to the secondary national highway design standard in Chinese Highway Engineering Technical Standard (JTGB01-2014) [51], the free-flow speed of all road is assumed to be vf = 60 km/h, and the traffic capacity is 15,000 pcu/day. The simulation duration is set to 35 days, with a single time step of 1 h, resulting in a total of 840 time steps.  
The road network consists of 12 OD pairs, 30 nodes, and 52 road segments. Among them, nodes 1, 2, and 3 serve as traffic demand attraction points, while Nodes 4, 5, 6, and 7 function as traffic demand generation points. The maximum demand flow for each OD pair is provided in Table 4.  
Figure 13: Four types of geological disaster classification examples.
## 2 Materials and Methods
\begin{array}{l}\sum\limits_{\mathrm{k}=1}^{\mathrm{N}}\mathrm{a_{k}}=0, \sum\limits_{\mathrm{k}=1}^{\mathrm{N}}\mathrm{b_{k}}=0\\ \sum\limits_{\mathrm{k}=1}^{\mathrm{N}}\mathrm{b_{k}}\sin(-\delta_{\mathrm{k} })=\sum\limits_{\mathrm{k}=1}^{\mathrm{N}}\mathrm{a_{k}}\cos(\delta_{\mathrm{k}}) \\ \sum\limits_{\mathrm{k}=1}^{\mathrm{N}}\mathrm{b_{k}}\cos(\delta_{\mathrm{k}}) =\sum\limits_{\mathrm{k}=1}^{\mathrm{N}}\mathrm{a_{k}}\sin(-\delta_{\mathrm{k} })\end{array}  \tag{9}\]  
Figure 2: The Hanning window function. (**a**) Shape of the Hanning window function; (**b**) evaluating function of the Hanning window in the thickness measurement; (**c**) evaluating function of the Hanning window in the front-surface measurement; (**d**) evaluating function of the Hanning window in rear-surface measurement. where \(\delta_{k}\) is the phase shifting value. According to the phase extraction design algorithm-characteristic polynomial method [27] and Equations (5-7), it is necessary to develop a new window function to satisfy Equation (9). We define a 36-step window function based on the characteristic polynomial design criteria and the phase shift value \(\delta{=2\pi/N}=\pi/4\) as follows:  
\[ \begin{array}{ll}w(36)=&\frac{1}{2460}(1,5,15,35,70,126,210,330,490,690,926,1190,\\ &1470,1750,2010,2260,2380,2460,2460,2380,2260,2010,\\ &1750,1470,1190,926,690,490,330,210,126,70,35,15,5,1)\end{array}  \tag{10}\]  
Figure 3 shows the window function and its evaluating function at different frequencies, respectively. The amplitude of the sidelobe of new window function of the 36-step algorithm is suppressed by approximately 0.066%, as shown in Figure 3a, which is superior to those for the Hanning window (2.68%). As mentioned above, in the actual measurement process it is difficult to measure a transparent plate with a thickness of less than 10 mm because of the limitation in the air-gap range for a Fizeau  
Figure 3: New window function of the 36-step algorithm. (**a**) Shape of the new window function of the 36-step algorithm defined by \(w(36)\); (**b**) new window function of the 36-step algorithm in the thickness measurement; (**c**) new window function of the 36-step algorithm in front-surface measurement; (**d**) new window function of the 36-step algorithm in the rear-surface measurement. type interferometer. In order to overcome this problem, we propose a novel method where a large value for \(M\) is used. This is achieved with the spectral stretching of the Discrete Fourier Transform function applied for the signal obtained for a transparent plate of an arbitrary thickness in the actual measurement process using the Fizeau interferometer with wavelength tuning. In this case  
\[M=r\bullet N+2,r=0,1,2,3\cdots, \tag{11}\]  
where \(N\) is related to the phase-shifting value, \(\delta=2\pi/N\). For example, the phase-shift value is set as \(\delta=\pi/4\), while \(N\) is 8. In this situation, one can measure the sample with \(M=2,10,18,26,34\cdots\) as in Equation (11). \(H\) can be set as \(H=3,15,27,39,51\cdots\) mm for the transparent plate with a thickness of 1 mm and a refractive index of 1.5.
# Towards the Detection of GPS Spoofing Attacks against Drones by Analyzing Camera's Video Stream
## 5 Real-World Evaluation
### Results
Figure 14: The FPR for a window size of 4 and 5 frames and an altitude of 50 m.  
Figure 15: The FPR for a window size of 5 and 6 frames and an altitude of 100 m.
## 5 Test Results and Analysis
### Inversion Results of NHT Method  
The cone tip resistance coefficient (\(N_{\text{m}}=Q_{\text{t}}\)) is the slope of the straight line in Figure 11, with the net cone tip resistance \(q_{\text{net}}\) in the stable penetration section as the ordinate and the effective overburden pressure as the abscissa, as shown in Figure 11a. In this example, if a straight line is forced through the origin (\(c^{\prime}=0\)), the normalized cone-tip resistance obtained is \(Q_{\text{t}}=2.03\), \(2.10\), \(2.11\), and \(2.13\). Similarly, the pore pressure parameter \(B_{\text{q}}\) is the slope of the straight line in the graph with the measured excess pore pressure \(\Delta u_{2}=u_{2}-u_{0}\) as the ordinate and \(q_{\text{net}}\) as the abscissa, and \(B_{\text{q}}=0.55\), \(0.67\), \(0.66\) and \(0.69\) are obtained, as shown in Figure 11b. Taking the values of \(B_{\text{q}}\) and \(Q_{\text{t}}\) into Figure 12, it can be concluded that silty clay's effective internal friction angle in the Yellow River Delta is \(26.8^{\circ}\) according to the NTH method. The triaxial test is carried out on undisturbed soil samples of soft clay, and the reference value of the effective internal friction angle in the laboratory is obtained and compared with the value obtained by the NTH method, as shown in Figure 13. The results show that the evaluation results of the NTH method are in good agreement with the laboratory results.  
Figure 10: Calibration chamber CPTu results.  
Figure 11: Normalized data calculation: (**a**) Solution of \(Q_{\rm d}\); (**b**) solution of \(B_{\rm q}\).
# Review on Electrical Impedance Tomography: Artificial Intelligence Methods and its Applications
## 2 Imaging Modalities of Electrical Impedance Tomography (EIT)
### Frequency-Difference  
The basic principle on which multi-frequency EIT (MFEIT) works is that impedance of every tissue has a unique frequency spectra and it allows the one-shot imaging deprived of the disadvantages that absolute imaging encounters. Spectral data can be reconstructed and achieved by calculating various frequencies at the same time or in rapid sequence. Similarly to time-difference, one measurement is obtained as a reference, other is taken at a different frequency, and then it will be reconstructed by inverting the Jacobian matrix.
## 4 Experimental Results
### Pre-Processing Result  
The input CT images are divided into blocks, and the unwanted non-informative blocks are removed for further pre-processing. The unwanted artifacts and noise present in the CT scan images are removed in order to improve the texture quality of the nodules. The unwanted artifacts are filtered using the nonlinear anisotropic diffusion filter. The sample input image obtained from the dataset, the pre-processed image using the anisotropic nonlinear diffusion filter, and the extracted texture features for the input image using the proposed research are illustrated in Figure 3.  
From Figure 3b, it can be seen that the anisotropic nonlinear diffusion filter reduces the noise and enhances the textures of the image. This helps in extracting the features in a better way for further analysis. The Peak Signal-to-Noise (PSNR) ratio is calculated to measure the effectiveness of the used pre-processing method. PSNR is the most commonly used metrics to measure the efficacy of the denoised/compressed images. The average PSNR obtained in this research is 34.56 dB.