esa-sceva/satcom-chunk-collection huggingface dataset:
# Camera Geolocation Using Digital Elevation Models in Hilly Area
## 5 Experiment and Analysis
### Localization Performance  
The experiment involved 284 photo skylines and 5041 DEM skylines in an area of about 200 km\({}^{2}\). The values of Top_1 to Top_100 items in the candidate list of correct locations were calculated. The photo skylines and the ground truth of DEM skylines were different in some details due to vegetation effects and error between locations (as illustrated in Figure 27).  
The skyline features of each image were extracted in turn, and then retrieved in the DEM skyline database, so that each image obtained a candidate list of DEM location points according to the matching  
Figure 27: Some differences between the photo and DEM skylines: (**a**) Photo skyline; (**b**) Extracted skyline (denoted in red) superimposed on the photo skyline; (**c**) Extracted skyline; and (**d**) The ground truth of DEM skyline.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Values** \\ \hline pitch & 4.49\({}^{\circ}\) \\ row & \(-\)0.26\({}^{\circ}\) \\ yaw & 356.42\({}^{\circ}\) \\ latitude & 28.15135\({}^{\circ}\) \\ longitude & 112.7574\({}^{\circ}\) \\ satellites & 10 \\ \hline \end{tabular}
\end{table}
Table 5: Location information of the image label.  
Figure 26: A photo taken in the experimental area.  
score. Similar to the method described by the authors of [18], we measured the performance of several localization methods by counting the proportion of the Top_n items in the candidate list containing the correct location. Figure 28 shows that the new algorithm had better performance.  
Although the proposed method performed better, the method failed on some images with a skyline which was only tens of meters away from the camera (as illustrated in Figure 29).
# Prediction of Machine Failure in Industry 4.0: A Hybrid CNN-LSTM Framework
## 3 Industry 4.0 Use Case  
Consider a typical use case of a smart factory having its production lines equipped with modern sensing technologies. All data produced by the sensors installed on production lines are acquired and accessible in real time. As depicted in Figure 1, a variety of modern AI techniques can be applied to the real-time data collected from sensors to get better insights into factory operations in real time.  
In a given smart manufacturing production line, the objective is to have an active response to demand and supply. Deep learning technologies can help with maximizing the efficiency of real-time processes when analyzing large amounts of data. They can be used to quickly analyse the relevant data and identify failures that can result in reduced production over a subsequent period of time. They provide information about the status of the equipment involved, thus supporting the human workforce maintaining and monitoring this equipment [33]. They also provide oversight for machine conditions and hence guarantee more reliability and process regularity, which eventually diminishes the necessity for regular and constant machine checkups and also reduces machine downtime [34; 35].  
In a typical smart production line, data from a large number of devices such as sensors and CCTVs (as shown in Figure 1) are collected and then processed into a more human-understandable form. The collected data are stored in scalable data storage systems. Big data techniques are used to pre-process and analyze the data. The data analysis can be presented through rich visualizations. Once the data are prepared, deep learning algorithms such as LSTMs are used, which identify the correlations between different features and take the appropriate steps based on the data. LSTMs are applied to the sensor data or time-series data to find anomalies within a production line. The LSTMs predict critical errors in advance to avoid a shutdown, fatal accident, or any unwanted event. The basic difference between LSTMs and other existing models is in terms of being more proactive and reactive. LSTMs are responsible for predictive maintenance, which can help many smart production lines to save millions of dollars in the support and maintenance of equipment. CNNs perform automatic feature extraction and can learn a high order representation of data (e.g., time-series or image-based data) [13]. However, CNNs alone when applied to time-series data are highly reliant on data pre-processing and tuning of a large pool of hyperparameters. In contrast, CNNs combined with LSTMs identify the most important features that contribute to the output. The combined model does not require heavy feature engineering, which makes the model computationally more reliable and efficient when compared to standalone CNNs.
## 4 Results and Discussion
### Finding the Optimum Number of Clusters
#### 4.1.1 Eight Clusters Evaluation Criteria  
The selected 55 audiograms are classified into 8 clusters using spectral clustering. The stage 1 silhouette plot indicates that seven audiograms are wrongly assigned, with one on the boundary between two clusters (very small negative Silhouette coefficient of \(-0.006072\)). In the second stage, the wrongly assigned audiograms are removed. The second stage Silhouette plot shows one wrongly assigned audiogram, which is removed in the third stage. The three stages plots are shown in Figure 3. To evaluate the number of clusters, the Eigenvalues were generated, indicating no gap. Thus, the authors calculated the Silhouette criterion values, Calinski-Harabasz clustering evaluation criterion, and Davies-Bouldin criterion values. The results are shown in Table 3, where the first stage has 55 audiograms and the evaluation criteria values are; SI = 0.3907, CHI = 36.7956 and DBI = 1.0427, stage 2 of 48 audiograms with SI = 0.464, CHI = 38.5503 and DBI = 0.9670  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**No of Clusters**} & **Criterion Values** & **Criterion Values** & **Criterion Values** \\  & **Stage 1** & **Stage 2** & **Stage 3** \\ \hline
7 & 0.35804 & 0.3985 & 0.4100 \\
8 & 0.39068 & 0.4319 & 0.4598 \\
9 & 0.35318 & 0.3924 & 0.4150 \\
10 & 0.38141 & 0.4239 & 0.4461 \\
11 & 0.31811 & 0.3650 & 0.3350 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Silhouette criteria clustering values stage 1 to stage 3 for a different number of clusters (testing 7–11 clusters).  
while stage 3 of 47 audiograms with SI = 0.4814, CHI = 38.5476, and DBI = 0.9426. These results indicate that the best clustering algorithm performance is for stage 3 where SI is the highest and DBI is the lowest.
# Search and Rescue in a Maze-like Environment with Ant and Dijkstra Algorithms
## 2 Background
### Wireless Sensor Networks (WSN) in SAR for Swarms of Drones  
#### 2.2.1 Mobile Wireless Networks  
Mobile wireless networks, i.e., networks where at least some of the nodes are mobile, have the potential to temporarily upgrade existing IT and communication infrastructure as and where needed [44; 45; 46; 47].  
Communication networks have to be dimensioned to accommodate peak demands, which implies that outside those periods, there is redundancy in the system. Given this redundancy, the ability to dynamically allocate additional capacity can dramatically increase the cost efficiency of the utilized resources. Due to this, telecom operators around the globe regularly deploy mobile cell towers during large public events (e.g., sporting events) or during times of known or predicted peak demand (e.g., New Year's Eve).
## 2 Operation Principle
The amplitude reflectance can be expressed as follows:  
\[R(\lambda)=r_{1}(\lambda)+\frac{\left(1-r_{1}^{2}(\lambda)\right)^{2}\cdot r_ {2}(\lambda)\cdot e^{i\Delta\varphi(\lambda)}}{1-r_{1}(\lambda)\cdot r_{2}( \lambda)\cdot e^{i\Delta\varphi(\lambda)}} \tag{5}\]  
Figure 2: The fabrication and sensing process of the proposed chirped Fabry–Pérot (F–P) interferometer. where \(r_{1}(\lambda)\) and \(r_{2}(\lambda)\) are the reflectance of the microfiber Bragg gratings inscribed on the two tapers. If we ignore the refractive index modulation depth difference induced by the UV absorption efficiency change, \(r_{1}\) (\(\lambda\)) and \(r_{2}\) (\(\lambda\)) can be approximated as equal. It should to be noted that Equation (5) is under the condition that the wavelength range of the two FBGs are inscribed at the exact, symmetrical position, which is very difficult to achieve. Otherwise, there will be no interference at the mismatched wavelength area at both the short wavelength region and the long wavelength region, as shown in Figure 3a. We can see that the two FBGs have the same length, but the left FBG (the red grating) is close to the zero point, while the right FBG is far from the zero point (the blue grating). Then, the short wavelength of the left FBG spectra is smaller than that of the right FBG spectra, and the long wavelength of the left FBG spectra is smaller than that of the right FBG spectra. The overlapping region (the dashed area in the spectra) is where the interference occurs, and there is no interference region in the short wavelength range. Unlike the traditional fabrication process of the F-P interferometer with two discrete FBGs, one long FBG inscribed across the two taper regions is used in our experiment, as shown in Figure 3b. We can see that the short wavelengths of the two FBGs are the same. At the zero point, the length of the cavity is zero, which means that the phase difference at the short wavelength is \(\pi\) (considering the half-wave loss). As the position moves away from the zero point, the phase difference increases with the wavelength, and the overlapping region (the dashed area) is the interference region. There will be extra spectra range for the right FBG, which can be easily filtered out by additional optical filters. When the tension is given to the displacement sensor, the profile shape function \(f(x)\) will change, and the reflective spectra will also change. Moreover, a microfiber can be more easily stretched than the conventional optical fiber due to its low stiffness, which is beneficial in the application of displacement sensing.
## 4 Discussion
### Applications to Mantle Metasomatism
#### 4.3.3 Compositional Evolution of the Reacting Melt  
A principal difference between the current experimental design and other experiments on metasomatic reactions is the inclusion of the carbon melt trap at the far end of the capsules in which melt compositions were analyzed. Although intended primarily to assist the flow of the melt through the peridotite, this melt trap collected the evolved melt. Initial melts entering the trap may have been artificially high in SiO\({}_{2}\), corresponding to a lower effective pressure because of the initial pore space in the trap [19]. After it was filled, the time series experiments how that the melt composition changed to approach the correct composition, with Ti, Na, K and Ba needing longer to reach equilibrium because of exchange reactions with the peridotite. Melts in the trap were inhomogeneous, varying between different pools that had experienced different paths through the peridotite (Figure 5).  
The compositions of melts in the trap show increased Mg# and lower BaO contents, K\({}_{2}\)O/Na\({}_{2}\)O and K/Al ratios relative to the initial lamproite melt composition (Figure 6) because of the loss of chemical components to phlogopite during melt/rock reaction. The Mg# would remain buffered at high values as long as melt percolates along grain boundaries rather than flowing through coated channels.  
The experiments could also be seen to represent an advanced stage of melting of phlogopite and K-richterite-bearing hydrous pyroxenite vein [18], from which the melt infiltrates the peridotite wall-rock and partially dissolves it [61]. At this advanced state of melting when phlogopite remains the only stable phase in the vein assemblage, the experiments confirm predictions [44] that the melt will dissolve mainly garnet and orthopyroxene. The melt in the carbon trap would then represent melt that moves on to higher levels of the lithosphere in the case of percolation along grain boundaries. This shows that the vein/wall-rock reaction is not a matter of simple dissolution of minerals, because phlogopite forms during the reaction, involving the dissolution of olivine. This would deplete the melt in K\({}_{2}\)O and BaO, which does not correspond to the extreme enrichment in K\({}_{2}\)O and BaO in lamproites [17]. However, this may be explained by the movement of melt through coated channels initially produced by high melt flow rates: lamproitic melt compositions are consistent with the melting of K-richterite-bearing rocks, which produce a large melt fraction with little rise in temperature above the solidus [18]. Following initial infiltration and reaction with wall rocks, melts with high K\({}_{2}\)O/Na\({}_{2}\)O will be channeled in the little-modified form to higher levels. The current experiments indicate that at lower rates of melt infiltration, alkaline melts with lower K\({}_{2}\)O/Na\({}_{2}\)O may evolve through melt/rock reactions.
## 4 Results and Analysis
### Energy Dissipation
Compared with \(\theta=0^{\circ}\), the \(\theta=20^{\circ}\) layout results, for example, in 6.3% more energy dissipation, while the \(\theta=-20^{\circ}\) layout leads to about 21.3% less dissipation. As shown in Figure 8c, all data follow a similar tendency. \(E_{L}\)/\(E_{0}\) is proportional to \(d_{c}\)/\(h\), and a correlation is presented to desirable their relationship. \[\frac{E_{L}}{E_{0}}=\eta\left(\frac{d_{c}}{h}\right)^{\zeta} \tag{19}\] \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \(\mathbf{\Lambda_{1}}\) & \(\mathbf{\Lambda_{2}}\) & \(\mathbf{\lambda_{3}}\) & \(\mathbf{\alpha_{1}}\) & \(\mathbf{\alpha_{2}}\) & \(\mathbf{\beta_{1}}\) & \(\mathbf{\beta_{2}}\) \\ \hline \(\theta\leq 0\) & \(0.2136\) & \(-0.0087\) & \(2.3731\) & \(0.1745\) & \(1.2072\) & \(-0.3784\) & \(-0.2083\) \\ \(\theta>0\) & \(0.1602\) & \(-0.0049\) & \(1.9210\) & \(0.7519\) & \(1.8548\) & \(-0.5718\) & \(-0.2284\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Coefficients in Equations (18), (20) and (21). Figure 8: Energy dissipation characteristics: (**a**) Velocity ratio, (**b**) simulated against predicted velocity ratio, (**c**) relative energy loss and (**d**) simulated against predicted energy loss. where \(\eta\) and \(\zeta\) = coefficients. Based on the simulated results, they are determined by  
\[\eta=\alpha_{1}\ln\left(\frac{h}{l}\frac{h}{H}\right)+\alpha_{2} \tag{20}\]  
\[\zeta=\beta_{1}\ln\left(\frac{l}{H}\right)+\beta_{2} \tag{21}\]  
where \(\alpha_{1}\), \(\alpha_{2}\), \(\beta_{1}\) and \(\beta_{2}\) are coefficients. Their values are shown in Table 4. Figure 8d compares the simulated _E1_/_E0_ values with the predicted ones. Equation (19) gives a reasonable estimation of the calculated _E1_/_E0_, as well as the reference data [11]. Most data fall within the \(\pm\)10% error limit. The average and max. discrepancy is 3.3% and 12.7%, respectively.
# Opportunistically Exploiting Internet of Things for Wireless Sensor Network Routing in Smart Cities
## 5 Simulation Details and Results
### Simulation Setup  
A two dimensional coordinate plane is used with 10 to 100 m, with multiple network configurations of 20 to 90 nodes, spread uniformly. There is one sink in the middle of the network. Mobile IoT devices randomly move in the region in straight lines. The radio power of each Sensor nodes and IoT device is \(-5\) dBm and 1 dBm, respectively. The simulation time of each run of the experiment is set to 915 s, and each experiment is repeated 20 times with random seeds. Considering the data rate guidance by [PERSON] et al. [6], Sensor nodes generates 20 bytes of data every 2 s. This data rate is specifically suitable for monitoring applications wherein data rate is consistent but not very high. As discussed by [PERSON] et al. [6], many application domains of WSN have similar data rates with most of the sensors generating data at a consistent frequency.
## 4 Results and Discussion
### Sensitivity of Parameters and Model Calibration
#### 4.2.2 Interaction Diameter of the SP  
The interaction diameter represents a regional domain in the channel, in which the SP can interact and redistribute the charge homogenously. This interaction obeys neutrality by conserving the mass and charge when leaving the interaction diameter range. The interaction between two SPs is essential for the charging process in the semi-solid electrode system.  
Figure 8: Changes in \(\beta_{Na}\) against varying \({actual}_{NSP}\) at the constant voltages of 0.75, 0.5, and 0.25 V.  
The SP size was kept constant at the optimized value of \(D_{Sp}\) = 99.3 \(\upmu\)m. The change in \(\beta_{Na}\) when changing the interaction diameter by \(\pm\)2 and 4% from the optimized value is illustrated in Figure 9. When the interaction diameter was less than the SP size, the \(\beta_{Na}\) increased because of the fewer scattering possibilities for other SPs. The numerical analysis showed that the relationship between SPs was asymptotic at the increased interaction distance. In contrast, for the interaction distance comparable to the size of SP, the interaction was most effective for the homogenous charging of the semi-solid electrode in the flow channel [62]. The change in \(\beta_{Na}\) at the lower voltages was almost negligible, yielding unproductive operating conditions. The utilization of carbon and charging productivity improved at the higher voltages, resulting in the more efficient charging of slurry electrodes.
## 2 Materials and Methods
### Statistical Analysis  
There were 60,936 individuals in the NHANES 2005-2016 data releases. The following exclusions were applied for the main analyses (in order of application) to include 37,552 individuals: age \(\leq\) 2 years (\(n\) = 6280), no day 2 dietary recall (\(n\) = 12,524), missing PIR data (\(n\) = 3042), average intake \(<\) 500 kcal (\(n\) = 113), average intake \(>\) 5000 kcal (\(n\) = 161), missing anthropometry data (\(n\) = 1261), and missing dietary intake data for day 1 (\(n\) = 3). Smaller numbers of individuals were included for biomarker measurements based on the years of the data available and the availability of measurements for individuals. The data release years and sample sizes for these analyses were as follows: urinary iodine (2005-2016; \(n\) = 15,412); red blood cell folate (2005-2016; \(n\) = 34,893); serum vitamin D (2009-2016; \(n\) = 22,549); serum copper, selenium, and zinc (2011-2016; \(n\) = 5157); serum B\({}_{12}\) (2005-2006 and 2011-2012; \(n\) = 9840); serum B\({}_{6}\) (2005-2010; \(n\) = 18,002); and MetS (2005-2016; \(n\) = 13,429). NHANES provided population weights for each of the data releases, and all analyses applied appropriate survey weighting for the variables [20]. Where possible, data from all data releases were used; however, for some of the urinary and blood measurements, these variables were only available for a smaller number of data releases as previously indicated, and the population weights were divided by the number of cycles utilized for the analysis. For descriptive statistics, frequencies and population-weighted percentages were calculated for dichotomous or categorical outcomes. Means and population-weighted standard errors were calculated for numerical outcomes. Arithmetic means were used for variables that were reasonably normally distributed upon visual inspection of the histogram. Geometric means were used for variables that had a skewed distribution. Multivariable logistic or linear regression analyses were used to model associations for dichotomous and numerical dependent variables, respectively, with age, gender, and PIR included as adjustment variables. Analyses were conducted using Stata software (version 15; StataCorp LLC, College Station, TX, USA).
## 3 Results and Discussion
### Furfural in Ex-Service Transformer Oil Samples  
The detection of furfural was assessed in real oil samples collected from two ex-service current transformers (CT.134 and CT.729). The insulating oil of these aged electrical devices is the same used for the previous assessment of the SPR sensor response. In Table 1 the concentration of some degradation products generated over years of transformers operation is reported. These products originate both from the oil oxidation, as for example gases, and from the degradation of cellulose, like alcohols and furfural (2-FAL).  
Figure 4 compares the transmission spectra at around 530 nm, obtained with fresh oil standards, and the spectra obtained with the CT.134 and the CT.729 oil samples directly contacted with the sensing surface (sensor P2) without any treatment or dilution. The transmission minimum wavelength is somewhat different from that reported in Figure 2, which was relative to a different sensor (P1). It is clearly seen from Figure 4 that the resonance wavelength is blue shifted at increasing concentration of 2-FAL in fresh oil. The spectra obtained with the two used oil samples are very similar to each other as expected because the 2-FAL concentration is similar (see Table 1) and they are blue-shifted with respect to that obtained in fresh oil, because of the presence of 2-FAL. The similarity of the transmission spectra in new and used oil samples shows that no interference from other compounds, deriving from the decomposition of paper or oil, occurs when using the POF-MIP sensor here proposed.  
For both the samples considered the analytical response was \(\Delta\lambda_{\mathrm{ex}}\) = 1.5 nm. Considering the non-linearity of the sensor response, the concentrations of the samples can be evaluated by the Hill equation [27], once the parameters are known, according to the following relationship deriving from Equation (5):\[c_{2-\mathrm{FAL}}=\frac{K_{d}}{\sqrt[n]{\frac{\Delta\lambda_{lim}}{\Delta\lambda_{ ex}}-1}} \tag{7}\]  
Using the parameters of the Hill equation reported in Table 2, the concentration was evaluated to be 0.17 ppm, with an uncertainty of 0.06 ppm, so the concentration found is not significantly different from that determined by the chromatographic analysis. The uncertainty is high, probably because the concentration is very near to the high detection limit. As the concentration determined by the sensor is in agreement with that determined independently, it must be deduced that the many other substances present in the samples, for example those reported in Table 1, do not interfere with the sensor response. This shows the very good selectivity of the proposed device in the considered samples, probably due to the high selectivity of the considered artificial receptor (MIP). The uncertainty is noticeably high, but it is sufficient for screening purposes, and in any case it could be improved simply by diluting the sample in order to bring the concentration well within the detection range.