esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Discussion
### Computational Complexity  
To demonstrate the advantages of the proposed method in terms of processing time and computational resources, the results are compared with the nonlinear system identification methods reviewed in Section 1. Table 11 provides a clear and detailed comparison of computational complexity and allows for evaluating the performance of different methods in terms of processing time and required resources.
## 3 Methodology
### Model Evaluation (Step 4)  
Accuracy, precision, and recall metrics were utilized to evaluate the performance of RF, SVM, and MLP. The accuracy calculates the proportion of correctly classified instances among the total number of instances in the dataset. Precision is a metric that measures the proportion of correctly predicted positive cases (true positives) among all instances predicted as positive, regardless of whether they were actually positive or negative. The recall metric focuses on the proportion of actual positive cases that are correctly identified by the model and is calculated as the ratio of true positives to the sum of true positives and false negatives [10; 26].
# Long-Distance Measurements Using a Chromatic Confocal Sensor
## 4 Methods
### Gaussian Fitting Method  
A similar approach was employed to fit a Gaussian distribution to the averaged spectral data. This fitting process was executed using a nonlinear, least-squares optimization method, as outlined in [28]. Specifically, the nonlinear regression technique was utilized to estimate the parameters \(\theta=\{\mu,\sigma,A\}\), where one symbol is the mean (or peak location), another symbol is the standard deviation (which defines the width of the Gaussian), and \(A\) is the amplitude of the distribution. The objective is to minimize the residual sum of squares between the observed spectral data points, \(y_{i}\), and the predicted values of the Gaussian model\(f\) (\(x_{i}\), \(\theta\)), formalized in the cost function  
\[S(\theta)=\sum_{i=1}^{n}[f(\theta;\mathbf{x_{i}})-y_{i}]^{2} \tag{10}\]  
Figure 9: Two captured spectra for different sample positions after low-pass filtering.  
where \(f\) (\(x_{i}\), \(\theta\)) represents the Gaussian function, typically given by  
\[f(x)=\frac{1}{\sigma\sqrt{2\pi}}e^{-\frac{1}{2}\left(\frac{x-p}{\sigma}\right)^{ 2}} \tag{11}\]  
The nonlinear least-squares fitting process aims to iteratively adjust the parameter set, \(\theta\), to minimize the difference between the model and the measured data points. This is typically achieved using optimization algorithms, such as method- or gradient-based techniques, which are well suited for problems involving nonlinearities in the parameter space. The optimal parameter values correspond to the best-fit Gaussian distribution that characterizes the averaged spectra. This allows for the critical properties of the spectra, such as the peak location (i.e., the maximum of the distribution given by \(\mu\)), to be precisely tracked. Monitoring these properties is essential for understanding spectral shifts.
# Low-Level Video Features as Predictors of Consumer Engagement in Multimedia Advertisement
## 3 Materials and Methods
### Observational Study  
An online observational study was conducted using the Clickworker crowdsourcing platform ([[https://www.clickworker.com/](https://www.clickworker.com/)]([https://www.clickworker.com/](https://www.clickworker.com/)), accessed on 20 November 2022) to collect data on consumer engagement with ads. The observational study is part of a broader investigation of consumer behavior related to in-video advertising. It covers multiple aspects of ad exposure, including engagement, attitude, and purchase intent, with psychometric measures of participants' mood, affective state, preferences, and demographics. Consequently, the number of materials used must be minimized to accommodate the scope of the study. In this article, the focus is on ad engagement.  
The target population was young adults (18-24 years old), native English speakers from the USA who engage in online multimedia more than any other age group, particularly ad-supported video streaming. 54% of the respondents were female and 46% male. 61% of them were white Caucasian, 8% Hispanic, 16% African American, 2% Native American, 10% Asian and 3% of other ethnicity. In addition, 70% were living in metropolitan and 30% in rural areas. The highest educational degree achieved was 2% primary, 50% secondary, 7% vocational, 35% bachelor's and 6% master's education. Furthermore, 47% of the respondents were students, 23% self-employed, 19% regularly employed and 11% unemployed. The reference ground truth for consumer engagement with ads was obtained using (UES-SF) and includes several aspects of engagement: _focused attention_, _esthetic appeal_, _perceived usability_, and _reward_[5]. Using UES-SF, each participant rated only one video ad randomly assigned to them. The number of raters per individual video was balanced (\(N_{rpv}=5\)). Responses were collected from a total of (\(N=150\)) participants.  
To control for the technology-related effects of multimedia exposure (e.g., screen size and technology-related usage behavior), only participants who used a personal computer were included in the study. This restriction also reduced the influence of ambient distractions in case of mobile media consumption. Participants were rewarded 2.50 Euro each for participating in the study. First, informed consent was obtained from the participants, informing them of the purpose of the study. They were then given a brief description of the purpose and duration of the study (average duration of five minutes). The participants were informed that no time restrictions were imposed on them, and were instructed to set an appropriate volume on their computer to fully experience the video ad. After viewing the ad, each participant was asked to rate their engagement using UES-SF. A content-related verification question has been implemented to make sure media content was indeed consumed by the participant.
# A Multi-Dimension Spatial Method for Topology Awareness and Multipath Generating
## 6 Related Work  
### Topology Awareness  
Topology awareness is an efficient methodology for topology match between overlay and underlay networks. P4P uses network coordinates to enhance forwarding with topology awareness [11]. ASAP search for the neighbor autonomous systems to obtain topology relations [31]. In [10], it is proved that coordinates would be sufficient for most applications requiring topology-awareness. Representative network positioning methods include GNP [24], Vivaldi [22] and MDS-MAP [21, 23]. However, the topology-aware solutions above generate network coordinates for all nodes together in a coordinate system, and do not consider the complicated bearing networks, which actually would reduce the accuracy of positioning and topology awareness.
## 4 Results and Discussion
### Simulation Setting  
We simulate our proposed routing protocol in ONE [28] simulator. The ONE simulator is implemented by Java and available as open source. Compared with other DTN simulators, the major advantage of the ONE simulator is that we can extend it to evaluate new protocols and algorithms including the proposed protocol in this study. As an open source project, it allows users to create scenarios based upon different synthetic movement models and real-world traces and offers a framework for implementing routing protocols. It supports simulating the mobility pattern of the nodes and the message exchange between them. Moreover, ONE simulator provides a variety of report types and analyzing modules.  
In this study, we choose an appropriate scenario to simulate our routing scheme for message transmitting. In order to evaluate the performance of Scheduling-PROPHET, we compare it with two typical protocols, Epidemic and PROPHET. In our simulation, we mainly focus on evaluating the performance with four metrics: Delivery Ratio, Delivery Delay, Overhead, Hop Count and Number of Dropped Packets [29; 30]. Our implementations run with the following settings (see Table 1). The mobile nodes are divided into six groups and move in a 4500 \(\times\) 3400 m\({}^{2}\) area of Helsinki city, which is a default scenario in ONE. Group1 and Group3 move with a speed uniformly about 0.5-1.5 m/s, as the pedestrians following the \"ShortestPathMapBasedMovement\" movement model. Group2 moves with a speed uniformly about 2.7-13.9 m/s, as an automobile driving on road. Group4, Group5 and Group6 move with a speed uniformly about 7-10 m/s, as trans driving on the given road--\"MapRoutMovement\". In our implementation, the speed for transmitting messages is 250 k/s with Bluetooth. We set \(\Delta\lambda\leq 10\) for comparing the possible paths. Then, we evaluate the protocols by the following three specific scenarios:  
* Evaluating the performance on delivery rate, overhead and the number of dropped packets, when the transmission radius varies from 4 m to 10 m according to the Bluetooth protocol.
* Evaluating the performance on delivery rate, delivery delay, overhead, hops and the number of dropped packets, when the node density varies from 12 to 645 in the network.
* Evaluating the performance on delivery rate, delivery delay, overhead, hops and the number of dropped packets, when the buffer size varies from 10 MB to 90 MB on each node.
## 4 Consistency Analysis
### Consistency Analysis Based on Hierarchical Clustering Method
#### 4.3.2 Inconsistent Coefficient  
The inconsistent coefficient of the hierarchical clustering method has a self-adaptability feature, and can show that cells should be divided into several categories according to the characteristics of the cells' sample data.  
Therefore, in this paper, the inconsistent coefficient should be calculated when the samples are merged into a new class before and after the vibration, respectively; then, the increment of inconsistent coefficients are obtained. If the increment is large after the class merging, this would indicate that  
Figure 14: Clustering tree. (**a**) Clustering tree before vibration test; (**b**) Clustering tree after vibration test.  
the two classes should not be able to merge into a new class, and that the previous class merging is reasonable. Therefore, the category of the previous class merging as a standard for cells should be divided into several categories. If results show that there are more categories of cells after the vibration test, the consistency of the cells which are deteriorated can be concluded.  
In this paper, the function INCONSISTENT of MATLAB was used to compute the inconsistent value of each non-leaf node in the hierarchical cluster tree \(Z\), which is a matrix generated by the function LINKAGE. The \(Y\) is a matrix with rows corresponding to each of the non-leaf nodes represented in \(Z\), therefore the inconsistent value of \(k\)-th (\(k\)-th indicates the number of batteries classified is \(k\)) class merging is defined as follows.  
\[Y(k,4)=\frac{Z(k,3)-Y(k,1)}{Y(k,2)}. \tag{27}\]  
The calculation results of the inconsistent coefficient are presented in Table 6.  
In the case of a cell classification as small as possible, according to Table 6, before the vibration, the increment on the inconsistent coefficient of the last five times of class merging is 0.29, 0.2, 0.48 and \(-\)0.27, while after the vibration, the increment values on the inconsistent coefficient of the last five times of class merging are 1.01, 0.29, 0.84 and \(-\)0.34. The maximum increment of the inconsistency coefficient is 0.48 before the vibration, and results show that, when the three classes are merged into the two classes, the increment of the inconsistent coefficient is the largest; therefore, cells being divided into three classes before vibration is reasonable. Similarly, cells being divided into five classes is more reasonable after vibration. Furthermore, it was found that the number of classes of cells after vibration are increased, which indicate that the consistency of cells is deteriorated.
# Investigation of the Effect of Capillary Barrier on Water-Oil Movement in Water Flooding
## 2 Oil-Water Two-Phase Flow Dynamics  
In this study, Navier-Stokes equations are adopted to characterize the oil-water flow dynamics, and the VOF (Volume of Fluid) method is used to obtain the spatial distribution of the individual phases. The equilibrium contact angle is used to characterize the rock wettability [32]. The governing equations for incompressible immiscible isothermal oil-water two-phase flow dynamics in rigid porous media are composed of three volume-averaged partial differential equations: the continuity equation (Equation (1)), momentum equation (the Navier-Stokes equation) (Equation (2)), and the fluid saturation equation (Equation (8)). Among these equations, the continuity equation is used to impose incompressibility constraint on each phase, the Navier-Stokes equation is adopted to describe water-oil two-phase flow dynamics in an Eulerian framework, and the fluid saturation equation is applied to implicitly capture the oil-water interface and to obtain the phase distribution. The pressure field and velocity field for each phase as well as the wetting-fluid saturation are calculated by solving these equations using the finite volume method. It is worth noting that the governing equations are derived through volume averaging the momentum equations within a control volume.
# A General Calibration Method for Dual-PTZ Cameras Based on Feedback Parameters
## 5 The Calibration of Extrinsic Parameters
### Translation Vector Solution
#### 5.2.3 Zoom-Translation Vector Solution  
Because the optical center moves back and forth along the optical axis in the process of zooming, the optical center changes relative to the original position during the zooming process. However, the actual distance of the camera's optical center changes by only a few millimeters, which can be ignored when high calibration accuracy is not required. A diagram of the change in the optical center caused by zooming is shown in Figure 6.  
The zoom-translation vector, \(\textbf{T}_{z}\), is consistent with the change in focal length. The maximum translation vector generated by the optical center is \(\textbf{T}_{z\_\_\_\_}\)max \(=(0\quad,0\quad,d_{\_\_}\)max \(-d_{\_}\)min\()^{T}\), with the camera zooming from the minimum to the maximum.  
Therefore, the zoom-translation vector as a function of focal length can be expressed as:  
\[\textbf{T}_{z}=\frac{f-f_{\_}\text{min}}{f_{\_}\text{max}-f_{\_}\text{min}} \textbf{T}_{z\_\_\_} \tag{20}\]  
where \(f_{\_}\)max and \(f_{\_}\)min are the focal length ranges of the camera in pixels, \(f\) is the current focal length estimated in Section 4.1, and \(d_{\_}\)max, \(d_{\_}\)min are the maximum and minimum values of the actual focal length in mm, which are the inherent parameters of the PTZ camera.  
According to the rotation formula, when the PTZ camera rotates \(\alpha\) degrees horizontally and \(\beta\) degrees in pitch and the zoom value is \(z\), the zoom-translation vector is:  
\[\textbf{T}_{r-z}=\textbf{R}_{v-\alpha}\textbf{R}_{h-\beta}\textbf{T}_{z} \tag{21}\]
# SVPWM Method for Multilevel Indirect Matrix Converter with Eliminate Common Mode Voltage
## 2 Multilevel Indirect Matrix Converter and the Proposed SVPWM Method
### The SVPWM Method for Elimination the CMV
#### 2.2.2 Inverter Stage Control
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **ON** & **Modulated** & & **Instantaneous** & \\
**Input** & & **Switch** & **Switches** & & **Split DC-Link** & **Average** \\
**Voltage** & **Sector** & & & & **Voltages** & **DC-Link** \\
**Phase \(\omega_{in}t\)** & & & **Upper Rectifier** & & **(\(d_{x}\), \(d_{y}\))** & \(V_{PO}\) & **Voltage \(v_{PN}\)** \\ \hline  & & & **Lower Rectifier** & & **(\(d_{x}\), \(d_{y}\))** & \(V_{ON}\) & \\ \hline \(-\pi/6\,\pi/6\) & I & \(S_{ap1}\) & (\(S_{bn1}\), \(S_{cn1}\)) & (\(-v_{b}\)/\(v_{a}\), \(-v_{c}\)/\(v_{a}\)) & \(v_{ab}\) & \(3V_{in}^{2}/v_{a}\) \\  & & \(S_{ap2}\) & (\(S_{bn2}\), \(S_{cn2}\)) & (\(-v_{b}\)/\(v_{a}\), \(-v_{c}\)/\(v_{a}\)) & \(v_{bc}\) & \(-3V_{in}^{2}/v_{c}\) \\ \(\pi/6\,\pi/2\) & II & \(S_{cn1}\) & (\(S_{bp1}\), \(S_{ap1}\)) & (\(-v_{b}\)/\(v_{c}\), \(-v_{a}\)/\(v_{c}\)) & \(v_{ac}\) & \(-3V_{in}^{2}/v_{c}\) \\  & & \(S_{cn2}\) & (\(S_{bp2}\), \(S_{ap2}\)) & (\(-v_{b}\)/\(v_{c}\), \(-v_{a}\)/\(v_{c}\)) & \(v_{bc}\) & \(3V_{in}^{2}/v_{b}\) \\ \(\pi/2.5\pi/6\) & III & \(S_{bp1}\) & (\(S_{cn1}\), \(S_{an1}\)) & (\(-v_{c}\)/\(v_{b}\), \(-v_{a}\)/\(v_{b}\)) & \(v_{ba}\) & \(3V_{in}^{2}/v_{b}\) \\  & & \(S_{bp2}\) & (\(S_{cn2}\), \(S_{an2}\)) & (\(-v_{c}\)/\(v_{b}\), \(-v_{a}\)/\(v_{b}\)) & \(v_{ca}\) & \(-3V_{in}^{2}/v_{a}\) \\
5\(\pi/6.7\pi/6\) & IV & \(S_{an1}\) & (\(S_{cp1}\), \(S_{bp1}\)) & (\(-v_{c}\)/\(v_{a}\), \(-v_{b}\)/\(v_{a}\)) & \(v_{ba}\) & \(-3V_{in}^{2}/v_{a}\) \\  & & \(S_{cp1}\) & (\(S_{an1}\), \(S_{bn1}\)) & (\(-v_{b}\)/\(v_{c}\), \(-v_{b}\)/\(v_{a}\)) & \(v_{cb}\) & \\
7\(\pi/6.9\pi/6\) & V & \(S_{cp2}\) & (\(S_{an2}\), \(S_{bn2}\)) & (\(-v_{c}\)/\(v_{b}\), \(-v_{a}\)/\(v_{b}\)) & \(v_{ca}\) & \(3V_{in}^{2}/v_{c}\) \\  & & \(S_{bn1}\) & (\(S_{cp1}\), \(S_{ap1}\)) & (\(-v_{c}\)/\(v_{b}\), \(-v_{a}\)/\(v_{b}\)) & \(v_{ab}\) & \(-3V_{in}^{2}/v_{b}\) \\
9\(\pi/6.11\pi/6\) & VI & \(S_{bn2}\) & (\(S_{cp2}\), \(S_{ap2}\)) & (\(-v_{c}\)/\(v_{b}\), \(-v_{a}\)/\(v_{b}\)) & \(v_{cb}\) & \(-3V_{in}^{2}/v_{b}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Switching state and average DC link voltage according to the input current sector.
# The Influence of Various Types of Functional Bread on Postprandial Glycemia in Healthy Adults
###### Abstract  
Bread is a crucial component of a balanced diet. Increasing the choice of functional bakery products based on whole grain flours, with the addition of seeds and grains, can improve health, including reducing postprandial glycemia and the risk of metabolic syndrome. The current study attempted to characterize the relationship between the composition and nutritional value of 23 different types of functional bread and postprandial glycemic response values. This study involved 209 non-obese healthy volunteers aged between 18 and 50. The study protocol followed the standard glycemic index (GI) method outlined by the International Standard, ISO 26642:2010. Most of the examined bread had a low GI and was composed mainly of rye, oats, buckwheat flour with a sourdough starter, and oilseeds. Postprandial glycemia was negatively associated with the fat, protein, and fiber content of bread. However, the GI depended directly on the carbohydrate content and, inversely, on the fat content in wheat bread and bread containing oilseeds. Similarly, using whole-grain flour and sourdough in a functional bakery reduces the GI. Adding oilseeds and sourdough to bread also reduced blood glucose levels approximately one hour after a meal. A greater number of ingredients in a recipe may be associated with a higher GI. In designing a functional bread with a potentially beneficial effect on postprandial glycemia, the nutritional value, type of fermentation, and additives (type and number) are worth considering. The high variability in postprandial glycemia after bread consumption is related to several factors and requires GI determination according to standard methods to ensure that the information provided to the consumer is reliable.  
glycemic index; postprandial glycemia; functional bread +
Footnote †: journal: Applied sciences
# Waste Silt as Filler in Hot Mix Asphalt: A Laboratory Characterization
## 3 Methods
### Phase II  
The mechanical properties of the produced HMA samples were determined using various tests, including the optimal binder content (OBC), Marshall stability, ITS and indirect tensile stiffness modulus (ITSM).  
The experimental HMA specimens contained 5.5% binder, 7% filler (C, SN or SC) and 87.5% aggregates (38% sand (0-4 mm) and 62% gravel (4-8 mm)). To evaluate the effect of filler type on the mechanical properties, HMA samples were prepared according to the Marshall method. The aggregate gradation was kept constant for all mixture types.  
The optimal binder content (OBC) of the mixtures containing untreated silt (labeled HMA-SN) and thermally treated silt (labeled HMA-SC) were determined (EN 12697-30). A total of 9 mixtures for each set was produced and the OBC was determined based on the highest Marshall stability (EN 12697-34).  
The mechanical characterization of the samples was conducted on specimens produced with the optimal binder content. The air voids content of the Marshall samples was calculated based on the EN 12697-8 standard. To study the resistance of the mixtures against moisture, a total of six samples (for each set) were produced using the Marshall method. Three samples were tested with dry conditions (25 \({}^{\circ}\)C) and the remaining three were tested after being submerged in water at 40 \({}^{\circ}\)C for 72 h. The indirect tensile strength (ITS) (EN 12697-23) test was then conducted on the 6 samples. The susceptibility of the mixtures to moisture was evaluated by comparing the ITS of wet and dry samples based on EN 12697-12. Moreover, all samples were tested in terms of indirect tensile stiffness modulus (ITSM, EN 12697-26 Annex C) at three different temperatures of 10, 20 and 30 \({}^{\circ}\)C to evaluate the possible change in the thermal sensitivity of the mixtures given by the addition of the waste fillers.
## 4 Results and Discussions
### Emerging Digital Technologies
#### 4.1.4 Internet of Things, Sensors, and Motion Capture
This connectivity enhances process efficiency, safety, and decision-making, while also enabling ambient intelligence and multimodal interactions. In contrast, sensors play a critical role in monitoring environmental and biometric parameters, contributing to hazard detection, safety compliance, and ergonomic assessments through continuous data collection and real-time alerts. Finally, MoCap technology enables real-time tracking of human movement and posture, supporting ergonomic analysis, robot programming,  
and seamless human-robot collaboration. Together, these technologies form a foundational layer for intelligent, responsive, and safe industrial environments. To sum up, IoT, sensors, and MoCap technologies function as the digital nervous system of smart factories. They enable the continuous monitoring of workers and the environment through real-time data collection and communication. Several studies confirm the role of the IoT in integrating real-time physiological and ergonomic data into OHS. For example, IoT devices and sensors continuously collect environmental and physiological data, while MoCap systems support posture and movement tracking. These technologies provide the data required for real-time DT updates and AI-driven analyses. When this continuous stream of data is fed into DTs and AI systems, it aids in enabling advanced predictive analytics, ergonomic assessments, risk detection, and adaptive safety interventions. Together, these technologies create a responsive and intelligent workplace that prioritizes worker well-being and operational efficiency.