esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Experiment 1: Text-Image Alignment and Contextual Coherence
#### 4.1.2 Performance Analysis: Metrics Across Domains  
The heat map in Figure 2 provides a detailed visualization of the performance of the AI model on multiple evaluation metrics, highlighting both strengths and areas of improvement. In the chemical category, Ideogram demonstrates relatively better performance in metrics such as formatting (3.30) and visual design (4.15). However, all models face significant challenges with text accuracy, as seen in scores of 2.20 for DALL-E 3, 2.50 for Ideogram, and a low 1.95 for Stable Diffusion, indicating substantial difficulties in maintaining precision and coherence in this domain. The code category shows an overall weaker performance across models, with Ideogram scoring highest in formatting (3.07), while text accuracy remains particularly low for Stable Diffusion (1.25) and Ideogram (1.75), underscoring systemic struggles in generating reliable and accurate code-related text. In the flowchart category, Ideogram excels, achieving the highest scores in visual design (4.75) and readability (4.30), signifying its ability to produce polished, legible, and visually appealing outputs. Stable Diffusion also performs reasonably well in flowcharts, though it falls short of Ideogram's benchmarks. For the math category, DALL-E 3 and Ideogram show strength in formatting (3.55) and readability (above 3.75), while Stable Diffusion continues to trail, scoring particularly low in text accuracy (1.85) and visual design (3.35). Overall, the results highlight Ideogram's strong performance in flowcharts but also underscore persistent struggles with the accuracy of the text, particularly in the chemical and code categories.  
We compared performance scores (alignment and weighted scores) from GPT-4 and human evaluators across categories such as flow charts, mathematics, chemistry, and code to assess evaluator alignment. As shown in Figure 3, the model aligns closely with human evaluators in categories such as flow charts and mathematics, where differences are minimal. However, in more complex tasks such as chemistry and code, it provides stricter evaluations, particularly regarding structural clarity and alignment. These findings suggest that, while GPT-4 is a reliable evaluator of structured content, fine-tuning is needed to better match human perceptions in a specialized domain.  
Figure 2: Heatmap of evaluation metrics by category and model.
# Generic Methodology for Field Calibration of Nacelle-Based Wind Lidars
## 5 LOS Velocity Calibration and Uncertainties
### Reference Quantity  
The LOS velocity is calibrated by comparing it to a reference measurand \(V_{ref}\) located at the point of focus for a CW lidar and at the centre of the range gate for a pulsed one. \(V_{ref}\) is the projection of the wind velocity onto the LOS direction \(\theta_{los}\). \(V_{ref}\) requires calibrated instruments. A cup and a sonic anemometer--respectively for wind speed and direction--were used to derive \(V_{ref}\) as follows:  
\[V_{ref}=V_{hor}\cdot\cos\varphi\cdot\cos\underbrace{(\theta-\theta_{los})}_{= \theta_{l}} \tag{2}\]  
Figure 4: Field testing of the 5B (**left**) and ZDM (**right**): inclinometers calibration and beam trajectory verification.  
where \(V_{hor}\) is the HWS, \(\varphi\) is the physical tilt inclination of the lidar beam, \(\theta_{r}\) is the relative wind direction, i.e., the difference between the wind and the LOS directions respectively denoted \(\theta\) and \(\theta_{los}\). The definition of \(V_{ref}\) is based on time-average measurements of \(V_{hor}\), \(\theta\) and \(\varphi\)--in this study 10-min periods and vector averages were used. Wind speed and direction variations within the averaging period were therefore assumed to have a negligible impact on instantaneous values of \(V_{ref}\). Equation (2) implicitly neglects the contribution of the vertical component \(w\) of the wind vector to the LOS velocity. This approximation is acceptable since for tilt angles \(\varphi<2^{\circ}\) this contribution is \(\sim\)3%-\(w\) and in flat terrain \(w\ll V_{hor}\).  
Note that the cup anemometer was preferred for reference wind speed measurements due to the lack of formal uncertainty assessment procedure of sonic-measured wind speed in the current IEC-standards [14].
# Device-To-Device Communication in 5G Environment: Issues, Solutions, and Challenges
## 7 Challenges in D2D Communication
### Challenges in Security
#### 7.3.2 Nonrepudiation  
In the D2D network, it is crucial to maintain nonrepudiation so that data integrity is preserved. However, the current research on D2D communication lacks a complete security architecture that can be used for the device authentication and users. What is more, the cooperation from mobile nodes that leave and join the network with current network members is required to optimize network performance. Specifically, it is necessary to create a flexible and scalable authentication framework that supports a rapid authentication for mobile users when they are joining or leaving the network.
## 3 Specifying Behavioral Profiles in IoT
### MUD Model  
MUD files define the type of communications and access of a certain device in the form of policies or access control lists (ACLs). Some examples of these restrictions could be \"allow the communication to devices of the same manufacturer\", \"allow the access to a specific DNS service\", or \"deny the access for a specific port\". As already mentioned, MUD is based on YANG [27] standard to model such restrictions, and JSON [28] as a serialization format.  
A MUD file contains the \"mud\" and \"acls\" containers. The former defines different aspects related to the obtaining and validity period of a certain file. For example, the field \"mud-url\" identifies the MUD file itself, and the \"last-update\" specifies when the file was generated. In addition, the \"to-device-policy\" and \"from-device-policy\" containers represent access lists references by indicating the appropriate direction of a specific flow to define the communication pattern of the device. Consequently, the \"acls\" container defines those access control lists (ACLs). It should be noted that it is based on the YANG Data Model for Network Access Control Lists (ACLs) [27], which is augmented by the MUD standard to define more expressive ACLs. For example, the nodes \"manufacturer\" and \"same-manufacturer\" enable the definition of policies to allow or deny the interaction with devices from the same manufacturer. Other fields allow for referencing network components (e.g., \"controller\" or \"local-networks\") without the need to know the associated IP addresses.  
The MUD model enables a flexible approach for defining network access control lists. However, it should be noted that this flexibility leads to the need for defining translation or interpretation approaches to convert MUD policies into rules, which can be enforced by network components. Our SDN-based architecture provides a translation approach, so that MUD restrictions can be automatically deployed into SDN switches to enforce the device's intended use.
# Investigation of Different Storage Systems for Solar-Driven Organic Rankine Cycle
## 3 Results and Discussion
### Final Comparison of the Three Examined Storage Systems
About the payback period, for the case [\(A_{c}=160\) m\({}^{2}-V=8\) m\({}^{3}\)] it was 7.56 years for ceramics, 7.89 for PCM, and 8.55 for pure oil; for the case [\(A_{c}=180\) m\({}^{2}-V=8\) m\({}^{3}\)] it was 7.63 years for ceramics, 7.81 for PCM, and 8.85 for pure oil; and for the case [\(A_{c}=200\) m\({}^{2}-V=8\) m\({}^{3}\)] it was 7.84 years for ceramics, 7.99 for PCM, and 9.19 for pure oil. Finally, it can be said that the use of PCM can enhance more electricity production than the use of ceramics, but the use of ceramics is more beneficial financially, according to all the criteria. Generally, both PCM and ceramic rocks have similar behavior, and they are clearly better choices than the use of  
Figure 15: Depiction of the examined systems in terms of system efficiency vs. net present value for the 18 studied scenarios. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Storage Type** & \(V\) **(m\({}^{3}\))** & \(A_{c}\) **(m\({}^{2}\))** & **Symbol** & \(\eta_{sys}\) & **_NPV (kE)_** \\ \hline Ceramic & 8 & 200 & & 12.97\% & 132.36 \\ PCM & 8 & 200 & & 13.07\% & 131.83 \\ Ceramic & 8 & 180 & & 13.89\% & 129.73 \\ PCM & 8 & 180 & & 13.97\% & 128.66 \\ Ceramic & 8 & 160 & & 14.79\% & 123.52 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Summary of the Pareto front points of Figure 15.  
pure thermal oil. Another point that has to be stated regards the relatively small differences between the efficiency and the financial parameter values. These small differences indicated the trends among the examined cases, and they were able to show the most effective techniques in every case. Comparing the found results with others from the literature, it can be said that the found results accorded with other studies. For example, the use of rocks inside the tank was found to lead to a 30% improvement in the financial indicators of the system, according to [PERSON] et al. [14]. The present work found around a 25% improvement, which is a similar result. Moreover, the studies [18; 19] found performance enhancement at 18% and 20%, while this work indicated 13%. The other studies [18; 19] had a bit higher enhancement because they regarded small-scale systems where there were higher enhancement margins due to the non-optimized design. On the other hand, the baseline case of this work was an already optimized scenario so the 13% enhancement (maximum value) was an acceptable and promising one.
## 3 Proposed Method for Real-Time Mouse Data Protection Using Generative Adversarial Networks (GANs)
### Dataset Configuration  
In this paper, to demonstrate the effectiveness of the proposed mouse data protection method in image-based authentication, we simulated realistic attacks by having the same user attempt authentication twice on four types of image-based authentication pages. During authentication, mouse movement data were collected using the GetCursorPos() function. The dataset was created by removing any data points where the mouse coordinates did not change from the previous ones. Table 1 shows the number of sequences with actual movement. The dataset is labeled with a numbering system indicating the sequence and authentication page type. For example, Dataset A-1 and A-2 represent the mouse sequences from the first and second attempts on the same page.  
Each dataset consists of elapsed time, X-coordinate, and Y-coordinate features, all sorted by the sequence of mouse movements. Elapsed time refers to the time taken to move from the previous coordinate to the current one, and the X and Y coordinates refer to the absolute mouse positions on the screen. To ensure equal importance for each feature, all values were normalized to a range of 0-1 using the min-max normalization. Table 2 shows a sample of the normalized Dataset A-1.  
Figure 1: Overall flow of the proposed method for real-time mouse data protection using GANs.
# Water Impermeability of Self-Compacting Fly-Ash-Containing Concrete
## 2 Materials and Methods  
The research was carried out using Portland cement of CEM 42.5 R and active mineral additive fly ash (Ukraine Power Station). Fly ash with an initial specific surface of 253 m\({}^{2}\)/kg was activated by grinding in a laboratory ball mill to three values of specific surface area: 290, 390, and 400 m\({}^{2}\)/kg on average. The chemical composition of fly ash is shown in Table 1.  
A mixture of quartz sand of two fractions of 0.16 mm to 2 mm and 2 mm to 5 mm was used at various ratios. The grain fraction of 2 mm to 5 mm was conditionally accepted as coarse aggregate and the fraction of 0.16 mm to 2 mm as fine aggregate. Modified polycarboxylate ether Melflux 2651f was used as a superplasticizer.  
By changing the superplasticizer consumption, a concrete mix flow of 550 mm to 600 mm (class SF1 according to EN 12350-8:2019 [20]) was achieved. With such a flow, the concrete cone slump is 26-27 cm, as previous experiments have shown.  
Water separation of fresh concrete mixtures was determined after settling in a cylindrical vessel according to the EN 480-4:2005 [21] method.  
To determine the strength of self-compacting concrete, specimens were made in the form of prisms with dimensions of 4 cm \(\times\) 4 cm \(\times\) 16 cm. The specimens were hardened for testing in humid air conditions at 20 \(\pm\) 2 \({}^{\circ}\)C. The strength characteristics were measured using a test machine FP 100/1 with a 1000 kN load capacity and accuracy \(\delta=\pm\)1.0%. Strength characteristics of concrete specimens were determined according to EN 12390-3:2019 [22].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**SiO2** & **Al2O3** & **Fe2O3** & **CaO** & **MgO** & **R2O (Na2O + K2O)** & **L.O.I** \\ \hline
56.91 & 22.68 & 9.60 & 3.61 & 1.65 & 1.66 & 3.85 \\ \hline \end{tabular}
\end{table}
Table 1: Chemical composition of fly ash.  
The water impermeability of the concrete specimens was determined by the \"wet spot\" method in accordance with the Ukrainian standard. A setup was used to test 6 specimens simultaneously. Water was supplied to the bottom of the specimens, and the upper end of the specimen is observed. The specimens were made in cylindrical molds with an internal diameter of 150 mm. The height of the mold was 30 mm. During the test, the specimens were placed in the sockets of the measuring setup and fixed. The water pressure is increased gradually, starting from 0.2 MPa. The duration of the pressure change ranged from 1 to 5 min. At each stage, the specimens were kept under pressure for 4 h. The water impermeability of each specimen was evaluated at the maximum pressure when water had not yet penetrated the concrete. For a series of specimens, the assessment was made at the level at which four out of six specimens had not yet allowed water to penetrate.  
Experiments to determine the strength and water impermeability were performed on specimens at 28 days.  
The study of the influence of a set of factors on the properties of RPC was carried out using the methods of mathematical planning of experiments [23; 24].
# Online State of Health Estimation for Lithium-Ion Batteries Based on Support Vector Machine
## 4 Predicted Results and Analysis  
To investigate the predictive accuracy of SOH, experiment validations of four cells are conducted. Here, two different batteries Cell 1 and Cell 2 are adopted for the following discussion. The effective discharging and charging cycle number is 166 for Cell 1 and Cell 2. The SVM model is trained offline based on 60% charging cycle data, and the remaining charging data are applied for validation. In practice, all CC charging data are difficult to gain during the real-time SOH estimation because it is hardly possible to discharge the battery to its low threshold every time. Here, we also discuss the situation that only partial charging data can be obtained. In addition, another two cells, namely Cell 3 and Cell 4, are tested and their SOH estimation is conducted to validate the performance of the proposed algorithm.
## 4 Absolute Nodal Coordinates Formulation
### Validation of Elastic Forces Model of Thin Plate Pendulum  
The dynamic problem of a free-falling thin plate pendulum provides a robust benchmark to assess the accuracy and computational efficiency of finite element models under large deformation and rotational dynamics [27].  
In this study, the pendulum plate depicted in Figure 6 was modeled using \(8\times 8\) ANCF thin plate elements with an edge length of 0.3 m and a thickness of 0.01 m. The material properties included a mass density of 7810 kg/m\({}^{3}\), a Young's modulus of \(10^{5}\) Pa, and a Poisson's ratio of 0.3. Initially, the plate was in a straight configuration, with one corner constrained and connected to the ground via a spherical joint. Upon release, the plate fell under the influence of gravitational acceleration (\(g=9.81\) m/s\({}^{2}\)), showcasing its dynamic behavior and deformation over time.  
The governing equation of motion for this problem, expressed in terms of the constant mass matrix \(\mathbf{M}\), nonlinear elastic forces \(\mathbf{Q}_{e}\), and external forces \(\mathbf{Q}_{ex}\), is given as  
\[\mathbf{M}\ddot{\mathbf{e}}+\mathbf{Q}_{e}=\mathbf{Q}_{ex} \tag{40}\]  
Figure 6 illustrates the dynamic response of the thin plate pendulum at various time instances (\(t=0,0.1,0.2,\ldots,0.6\) s), highlighting the progressive deformation of the plate as it fell. The evolution of the displacement of the pendulum's corner point is shown in Figure 7, where the \(x,y,z\) coordinates over time reveal the nonlinear trajectory and significant deformation experienced by the plate.  
To ensure the validity of the simulation, an energy conservation analysis was performed, as shown in Figure 8. This figure presents the energy balance at each time step, including for the potential energy (\(U\)), strain energy (\(W\)), kinetic energy (\(T\)), and total energy. The consistent total energy across all time steps demonstrates that the ANCF plate element adheres to the work and energy principles, providing a reliable representation of the dynamic behavior of the system.  
Figure 6: Dynamic analysis of the straight pendulum.
# Computational Analysis of PDE-Based Shape Analysis Models by Exploring the Damped Wave Equation
## 6 Experiments on Shapes
### Gaussian Initial Condition  
As we studied in Section 5.2.2, we can expect that the Gaussian initial conditions will provide smoother solutions \(u(x,t)\). The reduction of the Gibbs phenomenon should be more beneficial for the second-order methods than for the IE method.  
To study this, we recreated the settings from Section 6.1 and changed the initial condition from the \(\delta\)-peak to the Gaussian distributed peak. We studied three different width parameters: \(\sigma=\{\varepsilon,1,5\}\). Here, \(\varepsilon\) is a small non-negative number and indicates the limit of \(\sigma\to 0\). Since our calculations were done in MATLAB, we would like to mention that MATLAB has a built-in machine \(\varepsilon\) with a value of \(\varepsilon_{\text{machine}}\approx 2.2204\cdot 10^{-16}\).  
As in the previous section, we changed the damping parameter \(k\) to be between \(10^{-3}\) and \(1\). The results can be seen in Figure 11. Again, we denote the different numerical solvers with the known colour scheme and keep the meaning of the dashed and solid lines. New to this plot are the symbols on the solid lines, which indicate the different values of \(\sigma\). The triangle belongs to \(\sigma\to 0\), the square to \(\sigma=1\) and with the circle, we indicate \(\sigma=5\).  
First, we want to mention that the results for \(\sigma\to 0\) are similar to the results obtained with the \(\delta\)-peak initial conditions (cf. Figure 10). According to the theory, this should be the case, but it is a good sign that our calculations verify this.  
Second, we notice that in both scenarios, with or without damping (\(k=0\)), the hit rate increases with rising \(\sigma\). There are two exceptions to this. One is the IE method. A resized version of this plot can be found in Figure 12. Here, the Gaussian initial condition with  
Figure 11: (**Top**) to (**bottom**) We see the hit rate at a geodesic error of \(0.25\) from the centaur shapes obtained by three different numerical schemes. The colours and rows distinguish between the different solvers we used: (red, (**top**)) IE, (blue, (**middle**)) CN and (green, (**bottom**)) SO\(|_{0}\). Solid lines indicate a changing damping parameter \(k\) and dashed lines for a fixed (\(k=0\)) one. The symbols distinguish between the different widths \(\sigma\) of the Gaussian distributed initial condition.  
\(\sigma=1\) is slightly better than the Gaussian initial condition with \(\sigma=5\). The second exception can be observed in the interval of \(k\in[0.03,0.4]\), where \(\sigma\to 0\) performs slightly better than \(\sigma=1\). However, in the end, this means a nearly negligible plus of \(\approx\)1%.  
For the second-order schemes, we can still notice the maximum value for the hit rate around \(k=0.1\). Even the changes in \(\sigma\) did not affect this value.  
In the end, we can conclude that all numerical methods benefit from the Gaussian initial condition. The second-order schemes profit even more than the first-order method.
# A Novel Hybrid Unsupervised Learning Approach for Enhanced Cybersecurity in the IoT
## 5 Results
### Performance Analysis of Proposed Method
#### 5.6.2 Performance Analysis of Proposed Method  
OCSVM demonstrates high recall and specificity, leading to a low false negative rate (FNR), indicating slightly superior performance compared to dAE and DBSCAN. The dAE method achieves good recall and specificity, albeit with a slightly higher false negative rate (FNR). In contrast, DBSCAN exhibits lower recall and specificity than OCSVM and dAE, resulting in a higher false negative rate (FNR). Among the three methods, OCSVM performs exceptionally well, particularly in recall and achieving a low false negative rate, demonstrating strong capability in detecting actual anomalies. It is thus considered a recommended method for stage 1 detection.
# New and Efficient Algorithms for Producing Frequent Itemsets with the Map-Reduce Framework
## Mining Closed Frequent Itemsets with Map-Reduce
### The Algorithm
#### 3.2.2 Definitions  
To better understand the algorithm, we need some definitions:  
**Definition 1**.: _Let \(p\) be an itemset, and let \(c\) be a closed itemset, such that \(h(p)=c\), then \(p\) is called a generator of \(c\)._  
Note, that a closed itemset might have more than one generator: in the example above, both \(\{c\}\) and \(\{f\}\) are the generators of \(\{c,f\}\).  
**Definition 2**.: _An execution of a map function on a single transaction is called a map task._  
**Definition 3**.: _An execution of a reduce function on a specific key is called a reduce task._
## 5 Analysis of Test Results
### Pore Size Distribution of Synthetic Specimens and Natural Specimens  
In order to examine the difference between synthetic specimens and natural specimens, we carried out scanning-electron microscope assessment of the synthetic pure rock salt, synthetic pure mudstone, natural pure rock salt and natural pure mudstone. Some images are shown in Figure 13.  
From Figure 13a,b, we can see that both the natural pure rock salt and the synthetic pure rock salt have a lattice crystal structure. The crystal lattice of the natural salt rock is larger than that of the synthetic mudstone, and the arrangement of natural pure rock salt is more disordered; this is because, in the process of communution then remolding, the natural salt rock lattice is shattered and rearranged. From Figure 13c,d, we know that the synthetic mudstone is more closely arranged between particles.  
We show the pore size distribution of synthetic pure rock salt, synthetic pure mudstone, natural pure rock salt and natural pure mudstone by mercury intrusion porosimetry (MIP) in Figure 14.  
From Figure 14a,b, the intrusion and extrusion of mercury in the natural pure rock salt and the synthetic pure rock salt is mainly controlled by the 10-nm pore size, and the two are in good agreement. From Figure 14c,d, we can see that the intrusion and extrusion of mercury in the natural pure mudstone and the synthetic pure mudstone are mainly controlled by the 20-nm pore size, and the two are in good agreement.  
The SEM and the pore size distribution of synthetic specimens and natural specimens are similar, which shows that synthetic material permeability model is a reasonable analogue to the natural specimens.  
Figure 13: SEM pictures (2000 times). (**a**) Natural pure rock salt; (**b**) synthetic pure rock salt; (**c**) natural pure mudstone; (**d**) synthetic pure mudstone.