esa-sceva/satcom-chunk-collection huggingface dataset:
# A 3D Numerical Model to Estimate Lightning Types for PyroCb Thundercloud
## 3 Simulation Results and Discussion
### Impact of Wind Shear on PyroCb Thundercloud
The probable lightning types based on the polarity of simulated \(\sigma\) and their identified locations on the ground surface are summarized in Table 3. When \(d=2\) km, the value of \(\sigma\) remains negative over the simulation domain having an absolute peak value of 0.2531 nC/m\({}^{2}\) which is higher than the peak value measured for configuration 2 of pyroCb in Table 2. But its location on the  
Figure 7: Vertical cross-sectional representation of pyroCb thunderclouds incorporating the wind shear extension of SC and UP charge layers when (**a**) \(d=2\), (**b**) \(d=4\), and (**c**) \(d=8\) km. ground surface is shifted from 15 km to 15.5 km along the \(y\)-axis while remaining fixed at 15 km along the \(x\)-axis. For \(d\geq 4\) km, the positive value of \(\sigma\) becomes visible for pyroCb, and its absolute peak value is enhanced with increasing values of \(d\), indicating a higher probability of an increase in the \(-\)CG flash rate. As indicated in Table 3, the coordinates of the peak negative \(\sigma\) location are notably distant from the center. This spatial distribution suggests that the \(-\)CG lightning strikes tend to occur away from the thundercloud core. On the other hand, the absolute peak value of negative \(\sigma\) starts to decrease when \(d>2\) km, with the position slightly shifting away from the core along the \(y\) direction. Figure 8: With the wind shear extension of SC and UP charge layers, projections of potential (**a1–a3**) and electric field (**b1–b3**) distributions in the conceptual pyroCb thundercloud. Figure 9: Graphs showing the electric potential \(V\) (MV) at maximum field point of pyroCb thundercloud (**a1–a3**) and its corresponding longitudinal electric field \(E_{z}\) (kV/m) (**b1–b3**) under the effect of wind shear. The symbol “x” in (**a1–a3**) represents the flash initiation point, and the red-dashed dotted line in (**b1–b3**) indicates the initiation threshold field. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Extension** & **Absolute Peak** & **Location on** & & **Probable** \\
**Parameter, d** & **Value of \(\sigma\)** & **Earth’s Surface,** & **Polarity of \(\sigma\)** & **Lightning Types** \\ (**km**) & **(nC/m2)** & **(\(\chi,y\)) km** & & \\ \hline
2 & 0.2531 & \((15,15.5)\) & Negative & +CG \\ \hline
4 & 0.0065 & \((15,7)\) & Positive & \(-\)CG \\
0.2035 & \((15,16)\) & Negative & +CG \\ \hline
8 & 0.0565 & \((13,8.5)\) & Positive & \(-\)CG \\
0.1309 & \((15,16.5)\) & Negative & +CG \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Effect of wind shear extension leading to possible lightning types in pyroCb thunderclouds.
# Improving GNSS Landslide Monitoring with the Use of Low-Cost MEMS Accelerometers
## 2 Principles of Acceleration Measurement and Application to Tilt Measurement  
In most accelerometer sensors, the operating principle is the same: the detection of inertia, i.e., the resistance to motion variation of a mass subjected to acceleration. The mass is suspended on an elastic spring, while some type of sensor detects its displacement with respect to the fixed structure of the device. For a conceptual diagram of a 3D mechanical accelerometer, we can use a sphere in the center of a cube, suspended by three springs (Figure 1). Moving the cube in space, the sphere will move inside, stretching and compressing the springs holding it in place. Measurement of the degree of compression of the springs makes it possible to determine whether there has been an acceleration and to quantify this in value and direction with respect to a reference body system.  
Figure 1: Conceptual scheme of a thee-axis accelerometer.  
A capacitive accelerometer uses, as a principle for detecting mass displacement, the variations in measurable capacity and voltage between its armatures under the effect of movement due to the acceleration of the proof mass.  
We can write the linear law that links the displacement \(x\) between the armatures, with respect to the initial distance \(d\), with the variation of capacity \(\Delta C\) and the voltage \(V_{X}\) compared to the initial values \(C_{0}\) and \(V_{0}\)[5; 8]:  
\[\frac{x}{d}=\frac{\Delta C}{C_{0}}=\frac{V_{x}}{V_{0}} \tag{1}\]  
The displacement \(x\) is proportional to the force \(F\) through the elastic constant \(k\):  
\[F=ma=kx;\hskip 28.452756 pta=\frac{k}{m}x=\frac{k}{m}\frac{d}{V_{0}}V_{x} \tag{2}\]  
If the body is subject only to the force of gravity, a triaxial accelerometer can measure the direction of the tilt angles \(\Omega\) and \(\Phi\), or their composition \(\Psi\), through measuring the three components of acceleration \(A_{x}\), \(A_{y}\), and \(A_{z}\).  
\[\left|g\right|=\sqrt{A_{x}^{2}+A_{y}^{2}+A_{z}^{2}} \tag{3}\]  
The tilt components can be obtained with the classic method of converting from rectangular to spherical polar coordinates (4) (Figure 2). It is not possible, though, to determine the yaw angle in this way.  
\[\begin{array}{l}\Omega=\arcsin\frac{A_{x}}{\sqrt{A_{x}^{2}+A_{y}^{2}+A_{z} ^{2}}}\\ \Phi=\arcsin\frac{A_{y}}{\sqrt{A_{x}^{2}+A_{y}^{2}+A_{z}^{2}}}\\ \Psi=\arcsin\frac{\sqrt{A_{x}^{2}+A_{y}^{2}}}{\sqrt{A_{x}^{2}+A_{y}^{2}+A_{z} ^{2}}}\end{array} \tag{4}\]  
Based on the electrical response generated, there are two families of accelerometers [9]: AC response (analog) and DC response (digital). DC accelerometers can also give zero Hz responses and can therefore be used both for static and dynamic measurements.  
The monitoring of the angular values with acceleration measurements is theoretically independent of time, as it does not require integrations as in the case of gyroscopes. This makes the accelerometric angular measurement time-independent.
## 2 Theoretical Background  
When a sound wave with a certain acoustic power \(W_{i}\) impinges a wall-partition, its energy is divided into three components. One portion of the power is reflected back (\(W_{r}\)), while another portion (\(W_{a}\)) is able to pass through the surface of the material. The energy that passes through the surface can be divided into two components \(W_{d}\) and \(W_{t}\). The component \(W_{d}\) represents the part of the absorbed energy actually converted into heat due to the internal friction and viscoelastic effects. The component \(W_{t}\) represents the portion of the energy that passes through the partition and it is related to the power transmitted through the wall. The relation among incident, reflected and absorbed power is  
\[W_{i}=W_{r}+W_{a}=W_{r}+W_{d}+W_{t} \tag{1}\]  
as depicted in Figure 2. By dividing the single components for the incident power \(W_{i}\), the sound reflection coefficient, the sound dissipation coefficient \(\delta\), and the sound transmission coefficient \(\tau\) are defined:  
\[r =W_{r}/W_{i} \tag{2}\] \[\delta =W_{d}/W_{i}\] (3) \[\tau =W_{t}/W_{i} \tag{4}\]  
Building acoustics usually investigates the frequency range going from 100 Hz to 3150 Hz [18]. The reason of the 100 Hz lower frequency limit is that, in general, the first speech tones range between 100 Hz and 125 Hz for men, and they are an octave higher for women. As concerns the emission due to traffic noise, the encompassed frequency range is 125-2500 Hz and depends on the vehicles' speed. The two main properties to be considered for indoor acoustic comfort are the apparent sound absorption coefficient \(\alpha\) and the sound transmission loss \(TL\). The apparent absorption coefficient is defined as:  
\[\alpha=1-r \tag{5}\]  
and it represents the portion of incident energy absorbed (or not reflected) by the partition. In practice, sound absorbing materials and structures reduce the possibility of multiple reflections and are able to 'clean' the indoor acoustic environment from the annoying effects of reverberation. Sound absorbing materials and structures can be classified as porous  
Figure 2: Decomposition of a sound wave \(W_{i}\) impinging a wall into its reflected \(W_{r}\), dissipated \(W_{d}\) and transmitted \(W_{t}\) components. The sum of the dissipated and transmitted components represents the absorbed \(W_{a}\) component.  
materials, acoustic resonators (Helmholtz resonators that include perforated and micro-perforated panels respectively), vibrating panels and mixed systems (Figure 3).
# NFV-Enabled Efficient Renewable and Non-Renewable Energy Management: Requirements and Algorithms
## 9 Evaluation
### Analysis for Small-Scale Scenarios  
#### 9.2.1 Results in Small-Scale Scenarios  
The evaluation of OptTs and FastTs is presented in Figures 14 and 15, and it shows that the operation of the strategies contributes to a better use of \(P_{ES}\), which is reflected in a minimization of \(P_{RES}\) and \(P_{LACK(AR=100\%)}\), and in an increase of \(AR\). In all scenarios, OptTs and FastTs improve the performance of the system by enabling the processing of services that could not be executed under normal conditions (\(T_{s}^{k}=0\)). As the value of time-shifting increases, the system has more facility to move the workloads and, progressively, it is able to use all \(P_{ES}\) (i.e., \(P_{RES}=0\) and \(AR=100\%\)). Table 7 summarizes the improvements (\(AR\) gain) achieved by OptTs and FastTs. The most notable improvement is obtained in _Case B_, in which, under normal conditions, no service could be executed due to lack of energy. In the random cases, instead, lower \(AR\) gains are reached when compared to _Case A_ and _Case B_, because the conditions of the scenarios make it such that it is not possible consume all \(P_{ES}\) or reach an \(AR=100\%\). An example of the combinations processed and the computational resources needed for the execution of the algorithmic strategies is presented in Figure 16 for Case B. In the scope of simulation, Figure 16 shows that _FastTs_ requires approximately 4% RAM usage and 44% CPU usage when compared to capacity used by \(OptTs\) to process 100% of energy demands.  
Figure 14: Performance evaluation of the OptTs and FastTs for Case A and Case B. Parameters: Based on Table 5.  
Figure 15: Performance evaluation of OptTs and FastTs for Random Cases in small-scale scenarios. Parameters: \(N=6\) and \(numPart=2\) for all cases, the rest of parameters according to Case C and Case D of Table 5.  
Regarding the application of FastTs in the HEMS scenario, Figure 12b shows that, even though the improvement in the AR metric is only 20%, as shown in Table 7, the application of the adaptive management model produces a reduction of the peak power. This operational feature in energy systems is essential to allow uniform power distribution among users and avoid extra generation from non-renewable sources. The evaluation in the HEMS scenario is also useful to verify that the FastTs strategy not only allows to exceed the limit in the number of services processed by OptTs, but also the maximum values of \(T_{s}^{k}\). In case F, a \(T_{s}^{k}=12\) h enables the processing of 95% of energy demands.
###### Abstract  
Technological advancement and growing interest in digitalizing education increased Augmented Reality (AR) use in education. However, previous research findings on AR's potential for knowledge acquisition are inconclusive. Furthermore, computer self-efficacy has seldom been evaluated. AR is frequently combined with game-based approaches (GAME), yet the specific impact of each feature, \"AR\" and \"GAME\", is often not differentiated in the research design. This work analyzed an AR game-based learning environment for science teaching. It was conducted with German pre-service teachers, assessing \"Knowledge\" and \"Computer Self-Efficacy\". These measures were used to analyze the effect of AR and GAME in four intervention groups. The results showed a significant time effect for all groups in both variables, indicating all intervention designs led to knowledge and self-efficacy gains. However, no interaction effect was found, indicating the groups did not significantly differ in their knowledge and self-efficacy gains over time. The results further indicate no clear advantage of either AR or GAME for the design of science teaching. However, AR and GAME also did not hinder learning and both led to successful knowledge and self-efficacy gains. This indicates that AR and game-based learning support the learning process and strengthen learners' computer self-efficacy. Combining both features aids in easing the transition toward technology-enhanced learning by providing a playful learning experience, using digital as well as analog components.  
a 2025 15 2844  
the extant literature on AR and its application in science education, including the integration of game-based learning and the various methodologies used to assess its impact.
## 2 Improvements to the A* Algorithm  
### Traditional A* Algorithm  
The A* algorithm is a heuristic technique employed to identify the shortest path. The initial node serves as the starting point, expanding to the surrounding domain and selecting the node with the lowest cost after evaluation through a heuristic function. With this node, the expansion continues around, and subsequently, the node with the lowest cost is selected. The above steps are repeated until the selected node coincides with the goal point. The cost function employed in the A* algorithm is expressed as follows:  
\[f(u)=g(u)+h(u) \tag{1}\]  
Let u represent the current node. The term \(f(u)\) denotes the total cost from the starting point to the goal. Additionally, \(g(u)\) indicates the actual cost incurred from the starting point to the current node, while \(h(u)\) estimates the cost from the current node to the goal. The efficiency and search quality of the A* algorithm largely rely on the selected heuristic function. Common heuristic functions for the distance functions used in the A* algorithm include the Manhattan, Euclidean, and Chebyshev functions, which are denoted as follows. The Euclidean distance yields the paths of the best quality. Therefore, the heuristic function formula utilized in this paper is the Euclidean distance function. In Equations (2) to (4), \(lc\)and \(lg\) represent the x-coordinate and y-coordinate of the starting point, respectively, while \(tc\) and \(tg\) represent the x-coordinate and y-coordinate of the endpoint, respectively.  
\[f(u)= |lc-tc|+|lg-tg| \tag{2}\]  
\[f(u)=\sqrt{(lc-tc)^{2}+(lg-tg)^{2}} \tag{3}\]  
\[f(u)=|lc-tc|+|lg-tg|+\ (\sqrt{2}-2)\text{min}(lc,lg-tg) \tag{4}\]
# Temperature Impact on Engineered Cementitious Composite Containing Basalt Fibers
## 2 Materials and Methods  
### Materials  
The materials used in this experimental study for the production of ECC mixtures were Type I ordinary Portland cement (Sepahan Co.) complying with the requirements of the ASTM C150 standard, fine silica sand, class F normal fly ash complying with ASTM C618 requirements, BFs with a 10 mm average length and 20-13 \(\upmu\)m in diameter, water, and superplasticizer to improve workability, which were all purchased or provided from local suppliers. Fine silica sand is the standard aggregate in ECC to achieve even dispersion of fibers and plane cracking pattern, while limited studies have used coarser sand [72; 73]. Around three-quarters of the composite's volume were occupied by aggregates. Hence, their quality and composition may adversely affect the durability and structural behavior of the hardened composite. The physical and chemical properties of the used fine aggregate are shown in Table 1. Fine silica sand with a maximum grain size of 180 \(\upmu\)m and an average grain size of 100 \(\upmu\)m was used as filler and aggregate.  
Adding BFs into ECC enhances the mechanical properties and reduces its brittleness. Unlike other fibers, such as steel or polypropylene, BFs are formed in the manufacturing industry in bundles and cut into required lengths. However, the chopped BFs do not have a uniform length and diameter as shown in Figure 1. It is normal to consider the average diameter and length of BFs that are introduced by the provided datasheet from the manufacturer [5; 74; 75]. The mechanical properties of the BFs supplied by the manufacturer are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**pH** & **Solubility** & **Moisture** & **Fluoride (PPM)** & **Calcium** & **Phosphorous** \\ \hline
4-7 & Minimum 85\% & Maximum 3\% & Maximum s1800 & Minimum 21\% & Minimum 17\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical properties of fine silica sand used in this study.
# LoRA-Tuned Multimodal RAG System for Technical Manual QA: A Case Study on Hyundai Staria
### Research Aims and Paper Organization  
This study aims to develop a domain-adaptive multimodal Retrieval-Augmented Generation (RAG) system that enhances the accuracy, accessibility, and context-awareness of question answering over large-scale structured technical manuals. Using Hyundai Staria maintenance manuals as a representative use case, the proposed system integrates both textual and visual data to address common challenges in navigating complex documentation. While the automotive domain serves as the primary focus in this study, the methodology and system architecture are designed to be extensible to other domains, including industrial, aerospace, and military technical documents.  
The specific research objectives are as follows. First, we develop a methodology to automatically extract and refine structured data from large-scale PDF manuals that combine both text and images. Second, we construct datasets for QA, RAG, and Multi-Turn scenarios, considering both single-turn and Multi-Turn question answering. Third, we design a retrieval model that captures semantic similarity between sentences and propose an improved RAG architecture optimized for domain-specific content. Fourth, we implement parameter-efficient training using the LoRA (Low-Rank Adaptation) fine-tuning technique, applied to the bLLossom-8B language model and the BAAI-bge-m3 embedding model. Fifth, we conduct a comprehensive evaluation using quantitative metrics such as BERTScore, ROUGE-L, and cosine similarity, as well as qualitative expert assessment. Sixth, we examine the potential applicability and scalability of the system to other technical domains, including but not limited to defense maintenance manuals.  
The organization of this paper is as follows. Section 2 reviews related work on multimodal RAG systems and domain-specific question answering. Section 3 details the methodology for constructing and preprocessing the maintenance manual dataset. Section 4 presents the architecture of the proposed multimodal RAG system and the LoRA-based fine-tuning strategy. Section 5 discusses the experimental results using the constructed dataset, along with both quantitative and qualitative performance evaluations. Section 6 explores the broader applicability of the system to domains beyond automotive maintenance, including military technical documentation. Finally, Section 7 presents the conclusions and outlines directions for future research.
## 3 Results and Discussion
### Experiment Results
These evanescent waves encode subwavelength damage information, which is retrieved through the time-reversal and subspace orthogonality principles inherent to DORT-MUSIC. It should also be noted that DORT-MUSIC is more sensitive to experimental noise (\(\tau=-6.7\) dB), as shown in Figure 17. This noise-dependent behavior aligns precisely with [PERSON] et al. 's theoretical framework [38], whereas the distance between two scatterers decreases, the orthogonality of the signal subspace degrades, leading to a sharp decline in noise tolerance. The superior resolution of DORT-MUSIC highlights the critical role of noise control in practical applications. The most convenient method for reducing random noise is to use multiple measurements and averaging. In addition, deconvolution techniques, such as Wiener filtering, can also suppress noise and enhance damage-related components in the signal spectrum [41]. The advantages and disadvantages of these imaging methods are shown in Table 4, which provides clear guidance for method selection based on engineering needs: time-domain DAS is ideal for rapid inspections of large, simple structures like pipelines or storage tanks, where computational speed is critical, and moderate resolution is acceptable. Frequency-domain DAS is suitable for aerospace components where dispersion compensation improves accuracy for complex geometries. DORT-MUSIC is best for high-precision applications in controlled, high-SNR environments, such as detecting microcracks in turbine blades or composite materials, where near-diffraction-limit resolution is required despite higher computational costs. Simultaneously, when facing the detection requirements of large and complex structures, the application of frequency-domain imaging methods is limited by the need for structural dispersion relationships and even guided wave inversion calculations. While all methods involve trade-offs between resolution, scalability, and complexity, their complementary advantages enable a hierarchical monitoring strategy: using time-domain DAS for global surveillance and frequency-domain DAS or DORT-MUSIC for localized high-fidelity diagnostics in critical substructures. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Complexity** & **Computational Time** & **Resolution** & **Noise Sensitivity** \\ \hline Time-domain DAS & Low & Fast & Low & Moderate \\ Frequency-domain DAS & Moderate & Moderate & High & Moderate \\ DORT-MUSIC & High & Slow & Extremely High & High \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of three imaging methods. Figure 17: Comparison of the peak-to-center intensity difference indicators with different dual damage center distances by each imaging method. (**a**) Numerical simulation results. (**b**) Experimental results.
###### Abstract  
Nowadays, it is more and more important to deal with the potential security issues of internet-of-things (IoT). Indeed, using the physical layer features of IoT wireless signals to achieve individual identity authentication is an effective way to enhance the security of IoT. However, traditional classifiers need to know all the categories in advance to get the recognition models. Realistically, it is difficult to collect all types of samples, which will result in some mistakes that the unknown target class may be decided as a known one. Consequently, this paper constructs an improving open-categorical classification model based on the generative adversarial networks (OCC-GAN) to solve the above problems. Here, we have modified the loss function of the generative model G and the discriminative model D. Compared to the traditional GAN model which can generate the fake sample overlapping with the real samples, our proposed G model generates the fake samples as negative samples which are evenly surrounding with the real samples, while the D model learns to distinguish between real samples and fake samples. Besides, we add auxiliary training not only to gain a better recognition result but also to improve the efficiency of the model. Furthermore, Our proposed model is verified through experimental study. Compared to other common methods, such as one-class support vector machine (OC-SVM) and one-versus-rest support vector machine (OvR-SVM), the OCC-GAN model has a better performance. The recognition rate of the OCC-GAN model can reach more than 90% with a recall rate of 97% by the data of the IoT module.  
Keywords:internet-of-things; physical layer security; open-categorical classification; generative adversarial networks +
Footnote †: journal: Applied Sciences