esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Principal Component Analysis  
Hierarchical clustering (dendrogram), AHC of the paprika varieties presented in Figure 5, was conducted to find the inter-connectivity and closeness of the studied paprika samples and individual volatile organic compounds. The dendrogram cluster is divided into two groups, the first containing P1, P2, P3 and P5 and the second which contains P4, P6, P7 and P8. Paprika species from Romania and Turkey have similar chemical compositions, whereas samples from China, India, Morocco and Hungary can be differentiated. Pearson correlation regarding fatty acids confirms a very strong correlation between P1, P5, P2 and P3 and P7, P8, P4 and P6. The cluster analysis shows the differences between samples separated in each cluster based on the different content of fatty acids and volatile compounds.  
Figure 6 presents the principal component analysis regarding fatty acid, volatile compound and polyphenol correlation. The PCA is an unsupervised method to visualize the difference/similarity among sample profiles and detect significant variables contributing to these discrepancies among the eight paprika types. The data for PCA were evaluated for each class of fatty acids, volatile compounds and polyphenols as a preliminary test. Very strong positive correlations were obtained for C18:3(n6) with C16:1 (0.97), C18:0 (0.95) and C14:0 (0.92), and it was negatively correlated with C18:2(c + t)(n6) (\(-\)0.98). In addition, a strong positive correlation was obtained for C18:3(n3) with C18:0 (0.93), C16:1 (0.96) and C14:0 (0.95), and it was strongly negatively correlated with C18:2(c + t)(n6) (\(-\)0.95). A moderate correlation was obtained for C23:0 with C20:1 (0.71) (Figure 6a). Linoleic acid,  
Figure 5: Hierarchical clustering (dendrogram) of different paprika varieties.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Paprika** & **P1** & **P2** & **P3** & **P4** & **P5** & **P6** & **P7** & **P8** \\ \hline Polyphenol g GA kg\({}^{-1}\) & 8.35 \(\pm\) 0.79 & 9.44 \(\pm\) 0.96 & 10.9 \(\pm\) 1.1 & 8.18 \(\pm\) 0.82 & 5.61 \(\pm\) 0.61 & 7.81 \(\pm\) 0.81 & 7.44 \(\pm\) 0.73 & 5.11 \(\pm\) 0.52 \\ \hline \end{tabular}
\end{table}
Table 3: Total polyphenol contents in the studied paprika samples expressed as averages \(\pm\) standard deviation (\(n=3\)).  
C18:2, was positively correlated with the 18:1 isomer (0.95). Finally, C14:1 was positioned alone and was not related to any other components in the PCA.  
In the case of volatile compounds, four clusters can be observed. A very strong positive correlation was obtained for d-carvone with camphene and \(\beta\)-pinene (0.99). Four clusters of volatile compounds were identified (Figure 6b). The presence of aldehydes, alcohols, esters, hydrocarbons and ketones contributes to the flavor of paprika varieties originating from different countries.  
Figure 6: Principal component analysis loading plot of PC1 and PC2 for fatty acids **(a)** and volatiles and phenols **(b)** of different paprika varieties.
## 3 Results and Discussion
### Effect of the Roller's Rotational Speed on the Specific Energy at Different Feed Rates  
The consumed specific energy increased with the increase of the roller's rotational speed for all feeding rates, while the effect of the feeding rate on the consumed specific energy was non-significant, which caused an unclear impression on the diagram (Figure 9). It is noted that the consumed specific energy increased from 3.23 kW.h/ton to 4.06 kW.h/ton, 3.27 kW.h/ton to 3.74 kW.h/ton kW, 3.40 kW.h/ton to 3.80 kW.h/ton and 3.34 kW.h/ton to 3.98 kW.h/ton with the increasing roller rotational speed of 150 rpm, 200 rpm, 250 rpm and 300 rpm at feed rate levels of 600 kg/h, 700 kg/h, 800 kg/h and 900 kg/h, respectively. The unclear influence of the feed rate on the specific energy may have been due to the specific  
Figure 8: Effect of the roller’s rotational speed (rpm) on the power requirements (kW) at different feed rates (kg/h).  
Figure 7: Effect of the roller’s rotational speed (rpm) on the machine’s productivity (kg/h) at different feed rates (kg/h).  
energy, which depends upon the machine's productivity and power requirements, while the increase of the specific energy with the increasing roller rotational speed was because of the rate of the power requirement rising with the increasing roller speed, which was higher than the increase in the machine's productivity rate.
# Transportation Mode Selection Using Reinforcement Learning in Simulation of Urban Mobility
## 3 Materials and Methods  
In this study, trip generation, trip distribution, mode choice, and trip assignment, which are the steps of travel demand modeling, are partially discussed. For trip generation and trip distribution, the starting points on the simulation network were randomly assigned, but the destination points were fixed because they are an activity area [33; 34; 35; 36; 37; 38]. While this approach provides a simplified model at the campus scale, modeling based on more detailed demographic or economic data was not performed. Mode selection was performed by a reinforcement learning algorithm based on traffic density, travel time, and defined reward mechanisms (Gaussian and Poisson distributions). This method ensured that the choice of transport modes adapted to changing traffic conditions and optimized the efficiency of the overall system as well as individual users. Trip assignment was performed in the SUMO simulation environment by means of internal routing algorithms. These algorithms assign routes by optimizing the trips according to the shortest travel time and network conditions on the existing traffic network. These steps effectively enable the evaluation and comparison of transport modes under different traffic scenarios. However, it should be noted that the framework does not fully incorporate real-world travel demand patterns (e.g., user preferences, land use, or socioeconomic factors).
## 2 Materials and Methods  
### Animal Treatment and Experimental Design  
_Mytilus galloprovincialis_ (Lam.) were collected from Lake Ganzirri and Faro (Messina, eastern Sicily, Italy, 38\({}^{\circ}\)15\({}^{\prime}\) North, 15\({}^{\circ}\)36\({}^{\prime}\) East), transported to the laboratory under temperature- and humidity-controlled conditions, purified from epiboints, and acclimated for 15 days at a controlled temperature of 22 \({}^{\circ}\)C in naturally filtered seawater (\(\sim\)37% salinity; pH 8.1) that was continuously aerated (60 L h\({}^{-1}\)). Organisms were fed ad libitum with fresh cultures of _Nannochloropsis_ spp. or _Isochrysis galbana_ via an adjustable drip dispenser. Animals were housed in 50-L glass containers at a density of 1 specimen per liter and maintained under the above conditions. Organisms were treated with 5 nm silver nanoparticles (AgNP) or silver nitrate for 28 days. Three nominal exposure concentrations were considered (0.2, 2.0, and 20 ug L\({}^{-1}\)), as well as an unexposed population as a control reference. Two mesocosms with a capacity of 50 L per treatment were prepared to obtain two biological replicates. AgNPs were added daily to the experimental samples from a stable suspension of 1 g L\({}^{-1}\) prepared by the manufacturer (Amepox(r), Lodz, Poland) in ultrapure water. These particles have been described elsewhere [38; 39]. Silver nitrate from a stock solution of 1 g Ag+ L\({}^{-1}\) prepared in acidified ultrapure water was also used. Each day, 10% of the water content was changed, and a complete change was performed on the 15 th day. The organisms were fed ad libitum with fresh algal cells of _Nannochloropsis_ spp. or _Isochrysis galbana_. During exposure, physicochemical parameters (temperature and oxygen content) were regularly controlled and kept within a range of 20 \(\pm\) 1 \({}^{\circ}\)C; >8 mg L\({}^{-1}\) O\({}_{2}\). After exposure, the soft tissues of the mussels (digestive gland and gills) were collected and stored at \(-\)80 \({}^{\circ}\)C for subsequent biochemical analysis.  
Tissues were homogenized in an ice/water bath at 0-4 \({}^{\circ}\)C in homogenization buffer (0.5 M sucrose, 0.15 M NaCl, 0.02 M Tris HCl pH 8.0, 1 mM phenylmethylsulfonyl fluoride, 5 mM DTT) at a ratio of 1:6 \(w/v\) (one-part tissue plus five-parts buffer) for the digestive gland and 1:4 \(w/v\) (one-part tissue plus three-parts buffer) for the gills. The homogenates were centrifuged at 20,000\(\times\)\(g\) for 20 min at 4 \({}^{\circ}\)C, and the pellet (approximately mitochondria plus lysosomes) and supernatant--SN20--(approximately cytosol plus microsomes) were collected and stored at \(-\)80 \({}^{\circ}\)C until analysis. SN20 was used to detect the antioxidant response in mussel tissues if not otherwise stated.  
According to Directive 2010/63/EU of the European Parliament and of the Council in force in Europe, no specific regulation or approval by an ethics committee was required for the use of mussels.
## 4 Experiments and Results
### Utility  
In this section, we measure the performance of a differentially private frequent pattern mining algorithm on finding the top-\(k\) most frequent patterns. For this, we considered the F-score classification measure.  
Figure 4 illustrates the F-score obtained from the experiments using the _DP-FSM_ and _graph-based_ algorithms. We note in Figure 4A that the _F-score_ is not affected by the \(\varepsilon\) parameter for the _DP-FSM_ algorithm in the _Bank_ dataset, while in Figure 4B, we observe the influence of the parameter over the _F-score_. The same behavior is depicted for the _Bank_ and _German_ datasets. Besides, we observe the influence of the top-\(k\) parameter over the _F-score_: the smaller the \(k\) value, the lower the _F-score_ is.
## 4 Simulation Results and Discussion
### HR for the Resonance Frequency of 1000 Hz
Figure 9: The effects on the transmission loss with the resonator arrangement: (**a**) a serial arrangement of two resonators; (**b**) a parallel arrangement of two resonators; (**c**) effects of the wall distance of two resonators in a serial arrangement; (**d**) effects of the offset distance of two resonators in a parallel arrangement. The analysis results are depicted in Figure 9c,d. Note that, in the legend of the figures, GD and OD represent neck distance in parallel and serial arrangements, respectively, and the number means the additional distance to the baseline model. In the figure, it can be seen that a similar trend to the 500 Hz HR can be observed as the transmission loss increased with the increase in the offset distance in a parallel arrangement, and there was no distinct increment in a parallel arrangement. In the parallel arrangement result, however, an eminent difference is observed in the case of no offset distance as two peak transmission losses appear at 1000 Hz and 1250 Hz of the parallel arrangement. The peak at 1000 Hz becomes distinct as the offset distance becomes greater.
# Call Model and Test-Verification Methods for PS-LTE Core Equipment
## 3 Field Test Scenarios for MCPTT and eMBMS  
The MCPTT function is one of the most important functions among DSCN services, and ensuring stability for this function is important because it is an essential service to the field in case of various disasters. eMBMS is a service that can expand call capacity by switching to eMBMS when hundreds of people gather within a cell coverage during a rally or disaster situations, and DSCNs and call congestions occur through unicast communication.
# Does the Driver Understand the Warning? Comprehension of the Request to Intervene
## 3 Results
### Solaris Results  
Figures 5 and 6 provide information about the understanding of the Rtl message conveyed by each of the icons presented in Figure 2. Correct answers are marked with a green rectangle. All the icons turned out to be comprehensible to the drivers. The most problematic one was the lane departure icon (implemented in MobilEye). Only 60% of the participants understood its meaning, while the others read its message as \"the system is in control of the vehicle\" (30%) or \"the system has detected hands off the steering wheel\" (10%). Both yellow icons (designed by [PERSON]) scored high on comprehensibility (90% each), but it is worth noting that 10% of drivers understood them as conveying the opposite of their intended message (\"the system is in control of the vehicle). The same icon in red had a 100% level of comprehension, which may be related to the meaning of red in the automotive industry.  
Figure 4: Comparison of the Rtl alert level (ITS icons).  
As regards the understanding of what action should be performed when the icon is displayed (Figure 6), four out of the six tested pictograms received 100% of correct answers. The icon with the worst result was the steering wheel on a yellow background, only 60% of the drivers knew that when seeing it displayed (on the dashboard, Head-up Display, etc.), they should react to avoid an accident. Seventy percent of the respondents would have  
Figure 5: Comprehension Test, assessing the comprehension of the message conveyed by the icons (“What does this icon mean?”).  
Figure 6: Comprehension Test, assessing the level of understanding of the message conveyed by the icons (“What should you do when you see this icon?”).  
reacted correctly to the lane departure icon. Both pictograms can be considered moderately comprehensible to the average driver and therefore need to be refined.  
The participants were then asked to specify to what extent the presented icons conveyed Rtl (Figure 7). For this purpose, a simple scale from 1 to 5 was used, where 5 was the highest rating. The results show that the red car and the red pedestrian are the best pictograms. They clearly show that a dangerous situation is imminent (red color) and, at the same time, define the type of threat. The average rating given to these icons is 4.60. The third place also belongs to an icon in red, a warning triangle on a red background, with an average score of 3.70 (\(-\)0.90). The same pictogram on a yellow background scored 3.50, while the lane departure icon and the steering wheel icon were rated the worst (3.20 in both cases). Based on these and previous results, these two icons cannot be regarded as correctly representing Rtl.
# Cross-Correlation Wavelet-Domain-Based Particle Swarm Optimization for Lightning Mapping
## 2 Materials and Methods
### Database  
Simulations: The simulation was conducted to assess the performance of the proposed mapping using a simulated (ITF-VHF) signal. The details of lightning maps simulation are provided in our previous study [3]. An actual measured signal (from antenna B was used as a reference to simulate the other perpendicular antennae (C and D). The antennas C and D were copied of antenna B, with phase shift for each temporal sliding window. However, the handcrafted simulated lightning mapping relied on a fixed window size value, which affected the estimation of real lightning mapping. The PSO optimization was proposed for tuning the window size (\(W_{s}\)).  
Real lightning measurements: The data were collected using an ITF system [24], consisting of three sets of antennas, with a 15 m spaced two perpendicular baseline. The broadband ITF system was installed in Klebang Beach, Malacca Strait, Melaka, Malaysia (2.216304\({}^{\circ}\) N, 102.192288\({}^{\circ}\) E). Three samples of NBEs lightning flashes were collected for this study. The first NBE1 discharge occurred as the storm intensified on 4 December 2017, UTC+8 03:01:42. The second NBE2-initiated flash occurred sequentially with the NBE1 as the storm intensified, on 4 December 2017, UTC+8 03:21:13. The third NBE3 discharge occurred on 13 December 2017, UTC+8 05:08:16.  
Figure 1: The general structure of the proposed framework for ITF lightning maps optimization, based on PSO algorithm.
## 3 P-M Model
### S-P Sub-Model
#### 3.2.2 Price Pattern and Demand Function  
The energy market composed of PIES, the large power grid, and consumers can be described by a typical Price Leadership Model [30]. However, as the time-of-use electricity price of the large power grid does not change with the energy transaction in the actual PIES project, Equation (11) can describe the market behavior under this mode fully, instead of the Price Leadership Model.  
In actual engineering, Equation (12) is difficult to calculate because the real-time electricity price changes hourly. Additionally, the existing literature shows that the participation of consumers in real-time electricity price is very low [31]. Hence, it is impractical to implement real-time electricity pricing in the PIES. The time-of-use electricity price uses a static price scheme based on a predefined selling price, which usually maintains the same daily electricity price throughout the week, the month, the season, or the entire year [32]. The advantages of time-of-use pricing scheme include the following: (1) it is easy to follow; (2) it has a stable daily participating ratio; and (3) it can effectively shift the peak loads to valley loads [33]. Additionally, from Section 2, as the load and electricity cost price are divided into a peak segment and valley segment, a two-stage time-of-use electricity price is adopted to determine the electricity selling price of the PIES. The reasons for adopting the two-stage time-of-use electricity price will be also described in Section 4.1. Ref. [34] provides the cross-price function of the demand, as shown in Equation (14). Meanwhile, instead of Equation (12), Equation (14) is substituted into Equation (11).  
\[\left\{\begin{array}{c}Q_{v}^{e}=\alpha+\beta_{v}(Pr_{v}^{e})^{\gamma}+\beta
## 2 Problem Formulation and Preliminaries
### Preliminaries  
**Assumption 1**.: _The multiple disturbances are bounded with \(\left|d\right|\leq d_{\max}\), where \(d_{\max}\) is unknown positive constant._  
**Lemma 1** ([31]).: _Considering following system:_  
\[\dot{X}=A_{c}\left(t\right)X \tag{5}\]  
_where \(A_{c}\) is continuous and bounded. Let \(Q_{l}\left(t\right)\) be a bounded, positive, symmetric matrix, such as \(0<\lambda_{3}I\leq Q\left(t\right)\leq\lambda_{4}I\). There exists a symmetric matrix \(P\left(t\right)\) meeting:_  
\[A_{c}^{T}\left(t\right)P\left(t\right)+P\left(t\right)A_{c}\left(t\right)+\dot {P}\left(t\right)+Q\left(t\right)=0 \tag{6}\]  
_where \(P\left(t\right)=\int_{t}^{\infty}\hbar^{T}\left(t,t\right)Q\left(t\right) \hbar\left(t,t\right)d\), \(\left\|\hbar\left(t,t\right)\right\|\leq o_{1}e^{-o_{2}\left(t-t_{0}\right)}\), \(0<\lambda_{1}I\leq P\left(t\right)\leq\lambda_{2}I\)._  
Trajectory linearization control (TLC) [32; 33; 34; 35]: TLC has proven to be an effective robust control method, whose structure is shown in Figure 2. It is clearly noticed that TLC consists of an open-loop dynamic inversion and a linear time-varying (LTV) feedback regulator, in which the dynamic inversion transforms the tracking problem into a time-varying nonlinear tracking error adjustment problem, and the LTV regulator achieves robust stability and performance along the nominal trajectory. Hence, TLC produces robust stability and anti-interference ability, for which TLC has been widely applied to a model-scaled helicopter [33], a 6 DOF aircraft [34], and a car-like ground vehicle [35]. However, TLC is rarely used in the control of a USV.  
Figure 2: TLC scheme diagram.  
Neural network: For unknown continuous function \(\Delta f\left(X\right)\) (\(\mathbb{R}^{m}\rightarrow\mathbb{R}\)), it can be approximated over a compact set \(\Phi_{X}\subseteq\mathbb{R}^{m}\) with the following radial basis function neural network (RBFNN):  
\[\Delta f\left(X\right)=W^{*T}S\left(X\right)+\varepsilon \tag{7}\]  
where \(X\in\Phi_{X}\) denotes the input, \(W^{*}\in\mathbb{R}^{l}\) denotes the ideal weight, and \(l\) is the node number. From [36], we know that the number of NN nodes determines the approximation ability, and the ideal weight vector is written as:  
\[W^{*}=\arg\min_{\hat{W}}\left\{\sup_{X\in\overline{\epsilon}_{X}}\left|\Delta f \left(X\right)-\hat{W}^{T}S\left(X\right)\right|\right\} \tag{8}\]  
\(S\left(X\right)=\left[S_{1}\left(X\right),...,S_{l}\left(X\right)\right]^{T}\) represents the RBF vector, which is defined by:  
\[S_{i}\left(X\right)=\exp\left(-\frac{\left\|X-\rho_{i}\right\|^{2}}{2h_{i}^{2 }}\right),i=1,...,l \tag{9}\]  
where \(\rho_{i}\) and \(h_{i}\) are the center and spread, respectively, and the approximation error \(\varepsilon\) is bounded with \(\left|\varepsilon\right|\leq\bar{\varepsilon}\), where \(\bar{\varepsilon}\) is unknown positive constant.
## 2 Status of Research and Objectives in Radiant Tube Lifetime Modeling
### Recent Numerical Investigations  
[PERSON] et al. [26] and [PERSON] et al. [27; 28] performed the first studies on the thermomechanical behavior of radiant tubes using both the FVM and FEM models, thus forming a coupled fluid-structure-interaction (FSI) approach for radiant tube modeling. This FSI approach was validated with comprehensive temperature and flow measurements in a P-type radiant tube made of Alloy 602 with the burner operating at full-load. It was observed that an optimum geometry for a P-type radiant tube was one for which the R/D-ratio (radius of tube's elbow to the outer diameter) was close to unity. This helped to minimize both the temperature differences and the thermal stresses acting on the tube. Recent investigations focussed on an FSI approach, taking the transient temperature changes in the radiant tube during operation into account. [PERSON] et al. [29], for example, carried out a coupled FSI analysis for W-type Ni-Cr cast radiant tubes operating in a CGL. The tube deformation and the stresses in the tube were calculated by using the Norton creep model for Ni-Cr alloys. The accuracy of the creep simulation was, however, not validated against deformation measurements on the tube. In addition, the effect of alternating temperatures during tube operation was neglected. [PERSON] et al. [30; 31; 32] developed the available FSI approach further to include the effects of cyclic thermal loading caused by temperature changes due to burner operation (e.g., on/off firing) or process changes (change of strip speed or cross-section), using the extended Graham-Walles creep model [33; 34]. The investigations were validated against detailed integral and local deformation measurements on an Alloy 602 P-type radiant tube. The results showed an increase in creep deformation of the tube with an increase in the frequency of temperature changes for the same process time. The well-known phenomena of stress relaxation due to creep deformation were also confirmed in these studies.
# Deep Learning for Medical Image Cryptography: A Comprehensive Review
## 3 Vulnerability of Deep Learning-Based Medical Image Analysis
### Adversarial Attacks against Medical Images
#### 3.1.2 BIM (Basic Iterative Method) or I-FGSM  
An iterative and developing approach of FGSM is the BIM (basic iterative method) or I-FGSM [75]. When the picture is misclassified, it conducts an FGSM with a value and updates its value for T iterations with a minor perturbation. This approach is described as follows:  
\[{x_{t+1}}^{\prime}={x_{t}}^{\prime}+\alpha\times\text{sign }(\
abla_{x}\,\text{J}\,( \theta,{x_{t}}^{\prime},y)) \tag{2}\]  
where \(\alpha\)T = \(\epsilon\) and the \(\alpha\) is the magnitude of the perturbation for each iteration.