esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of a Chaotic Trajectory Generator Algorithm for Mobile Robots
## 5 Results  
In this section, the obtained results from the numerical simulations are reported. Starting the trajectory of the robot at the origin \((x,y,\theta)=(0,0,0)\), numerical simulations were carried out based on the proposed chaos generator algorithm with 200 and 300 iterations. The obtained results can be seen in Figure 8. The inner rectangle represents the boundaries  
Figure 6: Wheel speed histograms after dispersion in step 5: (**a**) dispersed left speeds \(v_{dl}\); (**b**) dispersed right speeds \(v_{dt}\).  
Figure 7: Reflective edge avoidance strategy.  
of the work area previously described. Each color represents a time lapse of two seconds during which the mobile robot uses the speed values obtained from the proposed algorithm (that is, for each iteration) before moving on to the next speed values.  
Figure 8 shows that the trajectories of the robot are apparently unpredictable and, with 300 iterations, the differential drive mobile robot covers (patrols) most of the work area. It is important to mention that, in the numerical simulations, the real dimensions of the robot are not considered, so the area actually covered by the robot is much larger than the one shown in the graphs. It can also be observed from these results (Figure 8), that the followed trajectory is the same in all cases under the same initial conditions, confirming the deterministic nature of the chaos. However, simulations to obtain Figure 9 were carried out using two different initial positions (in millimeters) with the same orientation \((\theta_{0}=0)\)\((x_{0},y_{0})=(0,0.01)\) in Figure 9a, and \((x_{0},y_{0})=(-350,500)\) in Figure 9b. For each case, 200 iterations of the designed algorithm were performed to compare the generated trajectories.  
Comparing Figures 8a and 9a, it can be observed that trajectories change drastically despite having a change of only 0.01 mm in the initial condition of the \(x\)-axis, demonstrating sensitivity to initial conditions typical of a chaotic system.
# Low-Level Video Features as Predictors of Consumer Engagement in Multimedia Advertisement
## 3 Materials and Methods  
### Video Ads  
The study was conducted using video ads. The selection criteria were developed in collaboration with Nielsen Company's media and marketing experts, taking into account the target audience and the different contexts of potential consumer engagement. The focus was on short-term ad exposure, which is common in ad-supported video streaming [6].  
The exclusion criteria for the video ads were: (a) content that depicted or promoted smoking, alcohol, or sex; (b) ads that did not promote an actual product but conveyed a social message (HIV testing, no drinking and driving) or promoted a service (e.g., dry cleaning, real estate companies, etc.); (c) low-resolution videos or very old video ads (produced before 1980); or (d) ads that promoted baby- or child-specific products because they were not relevant to the target audience. The inclusion criteria for the video ads were as follows: (a) language (only ads in English), (b) length (between 15 and 75 s in duration), and (c) accessibility (available online).  
Based on these criteria, 30 video ads were selected from YouTube. A complete list of videos with access links is provided in the referenced dataset. These video ads were later used to derive low-level video features relevant to modeling of consumer ad engagement.
###### Abstract  
The subgrade performance assessment and targeted maintenance of a highway during operation is very important and challenging. This paper focuses on the performance of the whole life-cycle of a highway subgrade during the operational period. Four roads with different traffic volume and geological conditions were selected; 20 test sections of these 4 roads were examined for a three-year distress survey, and 18 specific subgrade distresses of the 5 assessment objects were tracked and collected. First, based on the analytic hierarchy process (AHP), the subgrade performance of the selected section is evaluated, and the subgrade performance index (SPI) at different time periods is obtained. Then, based on the internal and external factors which affect the subgrade, three algorithms to determine the optimal support vector machine (SVM) model were proposed to train and predict the SPI. The results show that the SPI predicted results based on the data time series and particle swarm optimization-least squares SVM (PSO-LSSVM) model are better than those based on grid search (Grid-SVM) and genetic algorithm (GA-SVM) models. Finally, this paper provides a detailed idea for the rational layout of subgrade life-cycle assessment and decision-making by establishing a subgrade performance assessment-prediction-maintenance-management architecture system.  
subgrade distresses; analytic hierarchy process; support vector machine; life-cycle; subgrade maintenance +
Footnote †: journal: Applied Sciences
## 4 Experimental Evaluation
### Results and Analysis
#### 4.3.5 Effect of Self-Training Iteration Numbers  
To ensure the balance of performance and training times, we conduct experiments on different iteration numbers of self-training. Figure 5 displays the fluctuation trend of the F1-score, F1-score (all geological words), and F1-score (complex geological words) during a whole training process. For the F1-score (all geological words) and F1-score (complex geological word), in the first four iterations, they improve quickly, and after ten iterations, they progressively converge. For the F1-score, the convergence process is more stable.
## 1 Introduction
To this end, first, the dynamic response to ambient vibrations of this building has been evaluated through a network of ten high-precision axial accelerometers. Time-frequency domain analysis is used to identify the natural frequencies and modal shapes of the building. These results are compared with those obtained from remote monitoring through RAR. To improve the modal property recognition with RAR, the capability of this device to simultaneously measure different parts of the building [14; 25; 29; 30] is studied. In addition, in the processing of the displacement samples with respect to more classical methods, such as the power spectral density and periodograms, the use of other signal analysis methods, in the frequency domain, is proposed. In particular, methods based on the CPSD and the MSC that allow us to search for common frequency contents of signals with high levels of noise [31; 32; 33; 34; 35]. All calculations of these analyses were made with programs developed in MATLAB(r)[36] software. Figure 1 shows a scheme of the research methodology. A detailed description of each module is given in the following section, devoted to the methodology and to its application to a case study. Finally, based on the analyzed experimental results, the main advantages and limitations of the use of RAR to characterize the dynamic properties of conventional buildings are discussed. Other issues related to the frequency domain analyses of the obtained displacement-time histories are also presented. The potential of this device to detect structural damage, by comparing the fundamental period of structures in their undamaged and damaged state, is also discussed.
# Similarity Analysis for Time Series-Based 2D Temperature Measurement of Engine Exhaust Gas in TDLAT
## 4 Statistical-Based Mean Difference Test and Similarity Analysis
### [Step 1] Mean Difference Test
#### 4.1.1 Paired \(t\) Test  
The paired \(t\)-test is useful for analyzing the same set of items that were measured under two different conditions, i.e., differences in measurements made on the same subject before and after a treatment, or differences between two treatments given to the same subject.  
The paired \(t\)-test is based on the test statistic as shown below:  
\[T_{pt}=\frac{\overline{D}-d_{0}}{S_{D}/\sqrt{n}}, \tag{8}\]  
Figure 8: Procedure for mean different test and similarity analysis.  
where,  
\[\overline{D}=\frac{\sum_{k=1}^{n}\Bigl{(}T_{R\alpha c(i,A)^{k}}-T_{R\alpha c(i,B)^{ k}}\Bigr{)}}{n}, \tag{9}\]  
\[S_{D}=\sqrt{\frac{\sum_{k=1}^{n}\Bigl{(}T_{R\alpha c(i,A)^{k}-}T_{R\alpha c(i,B)^{ k}-}\overline{D}\Bigr{)}^{2}}{n-1}}, \tag{10}\]  
where, \(\overline{D}\) denotes the mean of the differences between the two kinds of cycles. \(d_{0}\) denotes a constant that can be set depending on whether the mean of the difference between the two groups is zero or a specific non-zero value. \(S_{D}\) denotes the standard deviation of differences between two kinds of cycles. \(T_{R\alpha c(i,A)^{k}}\) denotes the \(k\)th reconstruction temperature at \(i\)th time (s) of cycle A, \(i=1.0,1.1,\ 1.2,\ \cdots,\ 9.9\). \(T_{R\alpha c(i,B)^{k}}\) denotes the \(k\)th reconstruction temperature at \(i\)th time (s) of cycle B. \(n\) denotes the number of \(T_{R\alpha c}\) in \(i\)th time (s).  
For paired \(t\) test, the hypothesis is as follows:  
* Null hypothesis: \(H_{0}:\ \overline{D}=d_{0}\) (the population mean of the difference (\(\overline{D}\)) equals the hypothesized mean of the difference (\(d_{0}=0\)).
* Alternative hypothesis: \(H_{1}:\ \overline{D}\
eq d_{0}\) (the population mean of the difference (\(\overline{D}\)) does not equal the hypothesized mean of the difference (\(d_{0}=0\)).  
Here, the null hypothesis is rejected when the \(p\)-value is less than or equal to 0.05, i.e., when there is no difference between the mean of the two groups.
# Additive Manufacturing of PH 13-8 Mo Family: A Review
## 3 DED-Arc of PH 13-8 Mo Family
### Post-Processing Heat Treatments: Microstructure  
[PERSON] et al. [70] explored the formation of secondary phases at 600 \({}^{\circ}\)C (aging temperature) in DED-Arc-manufactured PH 13-8 Mo. The solution-treated material for 1 h at 1050 \({}^{\circ}\)C was subsequently aged at 600 \({}^{\circ}\)C for different dwell times: 20 min, 1, 2, and 4 h. It was found that the aging process started with the precipitation of \(\beta\)-NiAl by the end of the 20 min, whereas M\({}_{23}\)C\({}_{6}\) carbides were found in the martensitic microstructure by the end of the 1 h aging treatment. The formation of carbides increased the Ni content in the surrounding area of the carbide, which subsequently enabled the formation of reverted austenite. At the latest stage (4 h), new carbides were formed in the reverted austenite due to the higher carbon content in the FCC austenite. [PERSON] et al. [69] also conducted solution heat treatment at three different soak temperatures: 900, 1050, and 1200 \({}^{\circ}\)C. The temperature of solution treatment (900 \({}^{\circ}\)C) was not enough to eliminate the columnar structure and the \(\delta\)-ferrite. The temperature of solution treatment (1200 \({}^{\circ}\)C) was successful in removing the columnar structure; however, the temperature was too high, and it caused the \(\delta\)-ferrite phase to re-form. The temperature of solution treatment (1050 \({}^{\circ}\)C) was the most suitable solutionizing temperature, which resulted in a fully martensitic microstructure free from \(\delta\)-ferrite.  
[PERSON] et al. [71] also explored the influence of different heat treatments on the microstructure PH 13-8 Mo manufactured using DED-Arc. The samples were aged at 400, 450, 500, 550, and 600 \({}^{\circ}\)C for 4 h. The illustrations of the microstructure after each treatment are shown in Figure 5. It was possible to remove the unwanted \(\delta\)-ferrite with the solution heat treatment at 1050 \({}^{\circ}\)C, which resulted in a fully martensitic microstructure. There were no precipitates in the sample aged at 400 \({}^{\circ}\)C. However, \(\beta\)-NiAl precipitates were observed in the material aged at 500 \({}^{\circ}\)C and 450 \({}^{\circ}\)C. When the aging temperature was increased to 550 \({}^{\circ}\)C, not only \(\beta\)-NiAl precipitates but also Cr-rich M\({}_{23}\)C\({}_{6}\) carbides were present in the microstructure. Even further increased aging temperature (600 \({}^{\circ}\)C) caused the formation of \(\beta\)-NiAl precipitates, carbides, and blocky and elongated reverted austenite (Figure 5).  
[PERSON] et al. [72] developed a direct aging heat treatment procedure using DSC for the DED-Arc/PH 13-8 Mo. The sample aged at 495 \({}^{\circ}\)C for 45 min achieved the maximum hardness and was therefore chosen as the optimal post-processing heat treatment.  
Figure 4: As-built DED-Arc/PH 13-8 Mo wall (**a–c**) and its microstructure, which contains \(\delta\)-ferrite (**d**,**e**) and retained austenite (**f**,**g**) (Reprinted from Ref. [69], Copyright (2023), with permission from Elsevier).
## 3 Results
### Quantitative Histomorphometrical Evaluation  
In Table 1 and Figure 5, histomorphometrical evaluation revealed an overall increase in the NF-BA (%) from four weeks to 10 weeks in all the groups. At four weeks, the highest NF-BA (%) was observed in the BCP group followed by the BCP + HyA, HyA, and control groups. Statistically significant differences in the NF-BA (%) were observed between the BCP grafted groups and the non-grafted groups (\(p<0.05\)). Similarly, at 10 weeks, the NF-BA (%) in the BCP and BCP + HyA groups was significantly higher than that of the HyA and control groups (\(p<0.05\)). Interestingly, the addition of HyA to BCP did not significantly increase new bone formation as evidenced by the histomorphometrical findings. Along with an increase in NF-BA (%), there was a concomitant decrease in the R-BCP (%) in both the BCP and BCP + HyA groups.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Study Groups (n = 10 Per Group Per Time Period)**} & \multicolumn{4}{c}{**Bone Healing Time**} \\ \cline{2-5}  & \multicolumn{2}{c}{**4 Weeks**} & \multicolumn{2}{c}{**10 Weeks**} \\ \cline{2-5}  & **Newly Formed** & **Remaining BCP** & **Newly Formed** & **Remaining BCP** \\  & **Bone Area (\%)** & **(\%)** & **Bone Area (\%)** & **(\%)** \\ \hline BCP & 32.2 \(\pm\) 7.7 & 28.7 \(\pm\) 7.6 & 33.5 \(\pm\) 7.4 & 23.9 \(\pm\) 6.3 \\ BCP + HyA & 28.8 \(\pm\) 5.6 & 22.6 \(\pm\) 5.1 & 32.0 \(\pm\) 6.2 & 21.6 \(\pm\) 3.7 \\ HyA & 15.3 \(\pm\) 4.9 & - & 17.1 \(\pm\) 3.9 & - \\ Control & 8.6 \(\pm\) 2.5 & - & 10.6 \(\pm\) 4.5 & - \\ \hline \hline \end{tabular} BCP, biphasic calcium phosphate and HyA, hyaluronic acid.  
\end{table}
Table 1: Quantitative (mean \(\pm\) SD) histomorphometrical evaluation of newly formed bone area (%) and remaining BCP (%) in the different groups during the two time periods of evaluation.  
Figure 5: Bar-chart with mean and standard deviation based on histomorphometrical assessment showing bone area (BA%) for all study groups at 4 weeks and 10 weeks. (* indicates \(p<0.05\))
## 4 Results and Discussion
### Flow Kinematics
#### 4.2.2 Last Stage: Standing Wave  
As explained in Section 4.1, the last stage of this kind of flow when no violent impacts take place tends toward a typical viscous standing wave in the cases of the water and the glycerine. Two typical snapshots of the standing wave delayed half period for the water can be seen in Figure 12. The 2D periodic fluid problem can be modeled as a damped mass-spring system [51], where the mass is equal to the liquid mass, the spring constant is equal to the liquid mass times the square of the characteristic sloshing frequency \(\omega=\sqrt{gk\tanh kh_{l}}\), where \(k\) is the wave number associated with the distance between the vertical lateral walls and \(h_{l}\) is the liquid height, which is 3 cm for water. As in our case, the problem is confined between the lateral walls, and the analytical solution can be only considered an approximation. In this simplified mechanical model, the damping coefficient \(\beta=\beta(k,Re)\) depends on the wave number \(k\) and the Reynolds number \(Re=\rho\sqrt{gh_{l}}/\mu\); see [52] for details about the analytical solution. This analytical analogy can only be considered for the case of the water as the glycerine does not satisfy one of the main hypotheses of the analytical solution, which is \(Re\gg 1\).  
In Figure 13, the kinetic energy of the standing wave has been represented using the damped mass-spring model described above and also for the fluid kinetic energy given by Expression (21). It can be observed that the amplitude of the oscillation is more attenuated for the SPH solution, and this difference can be explained by the presence of surface tension and no-slip boundary conditions on the lateral walls in the SPH simulation. The effect of the lateral walls when viscosity is present was studied in [23]; in this work, the growth rate variation of the Rayleigh-Taylor instability was quantified by comparison between the  
Figure 11: Non-dimensional velocity magnitude of the central jets measured from the SPH computation and the experimental snapshots. The decreasing tank velocity is also plot to have a reference for the jet evolution.  
Figure 12: **(Left)**: Snapshots of the velocity contour for two instant separated half periods. **(Right)**: Mechanical analogy of the standing wave.  
confined and the periodic case with the same tank geometry. A lower growth rate value for two different geometries was observed in the confined case when compared to the periodic case for the dominant linear mode. The frequency given by the analytical model matches the SPH solution very well until the flow velocity is extremely low.
# A Semantic Framework for Decision Making in Forest Fire Emergencies
## 4 Ontology Overview
### Representation of Weather and Forecast Anomalies  
An anomaly refers to any occurrence that diverges from a typical form, contradicts expected patterns, or is generally perceived as unusual or peculiar in nature [37]. For all weather services, the forecast of significant weather events, such as wildfires, and associated anomalies is essential and provides important meteorological insight to a decision maker [38]. A representation of extreme weather forecasting information and anomalies is needed to understand the crisis and to allow different stakeholders to adopt and enable different early warning strategies. A well-known and popular indicator to estimate fire danger is the Canadian Forest Fire Weather Index (FWI) [39]. FWI belongs to the Canadian Forest Fire Weather Index System which comprises six standard components: three fuel moisture codes (Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), and Drought Code (DC)) and three fire behavior indexes (Initial Spread Index (ISI), Buildup Index (BUI), and Fire Weather Index (FWI)) [40]. The FWI, FFMC, DMC, and DC anomalies can be used to better assess the fire danger as historical values are calculated and analyzed from ONTO-SAFE's sources (i.e., earth observations and weather forecasts). The results of this analysis transformed into RDF triples and populated the ONTO-SAFE ontology properly. The involved concepts for weather and forecast anomalies are visualized in Figure 3. The class _Point_ is used to represent a point that presents some anomalies. Each point can have an FWI anomaly (via class _FWIAnomaly_), a Drought anomaly (via class _DroughtAnomaly_), a Duff Moisture anomaly (via class _DuffMoistureAnomaly_) and a Fine Fuel Moisture anomaly (via class _FineFuelMoistureAnomaly_) where the ontology presents numerous instances of the type Anomaly. In addition, each Point has a specific location (via class _Location_) and is linked with weather conditions (via class _WeatherConditionParameter_) and meteorological forecasts (via class _WeatherForecast_).  
Figure 3: Representation of weather and forecast anomalies in a specific point
## 3 Results  
In this section, we show some local deformation phenomena detected by InSAR analysis in several Italian coastal areas effectively covered by SAR data. Six study areas are analyzed. They are the coastal zones that include and surround Ravenna, Fiumicino, Campi Flegrei, Sibari, Augusta, and Taranto (Figure 2). We used C-band ENVISAT data provided by the European Space Agency (ESA) and X-band data acquired by the CSK missions of ASI. They are characterized by different resolutions, geometry of view, and spatial and temporal coverage, depending on the specific case study; thus, they provide an overview of the current and future scenarios of InSAR-based applications to the monitoring of coastal areas.  
Figure 2 reports an overview map of the Italian peninsula with the case studies discussed in this paper, while Table 1 summarizes the datasets, methods, and max deformation rates for each of the analyzed areas.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Area** & **Satellite** & **Orbit** & \begin{tabular}{c} **Time** \\ **Interval** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Images** \\ \end{tabular} & \
\begin{tabular}{c} **Processing** \\ **Method** \\ \end{tabular} &
\begin{tabular}{c} **Max Deformation** \\ **Rate [mm/year]** \\ \end{tabular} \\ \hline \hline
**Ravenna** & Envisat &
\begin{tabular}{c} Ascending \\ Desconding \\ \end{tabular} & 2003–2010 & 97 & SARScape-SBAS & \(-\)22 \\ \hline
**Fiumicino** & Envisat & Desconding & 2003–2010 & 38 & IPTA-MB & \(-\)6 \\ \hline
**Campi** & Cosmo-SkyMed &
\begin{tabular}{c} Ascending \\ Desconding \\ \end{tabular} & 2011–2015 & 320 & IPTA-MB & +50 \\ \hline
**Sibari** & Envisat & Ascending & 2003–2010 & 38 & SARScape-SBAS & \(-\)10 \\ \hline
**Augusta** & Envisat &
\begin{tabular}{c} Ascending \\ Desconding \\ \end{tabular} & 2003–2010 A & 88 & SARScape-SBAS & \(-\)8 \\ \hline
**Taranto** & Envisat & Ascending & 2003–2010 & 38 & IPTA-MB & \(-\)1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of datasets and methods used, and primary results. IPTA-MB: interferometric point target analysis multi-baseline; SAR: synthetic aperture radar; SBAS: small baseline subsets.  
Figure 2: Overview of the Italian peninsula. The red rectangles represent the coastal areas studied in this work.