esa-sceva/satcom-chunk-collection huggingface dataset:
# LoRA-Tuned Multimodal RAG System for Technical Manual QA: A Case Study on Hyundai Staria
## 6 Discussion  
### Analysis of Model Strengths and Limitations Based on Experimental Results  
The multimodal RAG-based maintenance question-answering system proposed in this study showed superior performance in all quantitative metrics compared to existing keyword search-based approaches. Particularly, achieving an 18.03%p improvement in ROUGE-L performance indicates significant improvements in sentence structure consistency and technical term accuracy required in the maintenance domain. Additionally, recording 89.7% procedural accuracy demonstrates achieving response quality that can contribute to actual maintenance feasibility and safety assurance.  
User evaluation also recorded high satisfaction with an average of 4.4 points, and the multimodal response method combining visual information was effective in simultaneously improving information clarity, practicality, and performance reliability. Since visual information is often essential in maintenance work characteristics, the multimodal integration approach of this system can serve as an alternative to compensate for the practical limitations of existing maintenance manual systems.  
On the other hand, problems were observed where the model had relatively lower response accuracy for queries involving complex conditions or multiple constraint conditions (e.g., operational situation queries with combined environmental, time, and functional conditions). Additionally, current image-text mapping is manually constructed with only 200 pairs, limiting coverage of various parts and situations, and accurate interpretation of complex electronic circuits or hydraulic system diagrams remains a challenge.
# Optimal Power Allocation with Hybrid Relaying Based on the Channel Condition
## 3 Optimal Power Allocation Schemes for Hybrid Relay Networks  
We propose optimal power allocation schemes for hybrid three-hop relay networks which maximize the achievable rate under a sum relay power constraint for given channel gains and transmit power from the source.
## 4 MODRL-SIA Algorithm  
### MODRL-SIA Algorithm Framework  
The MODRL-SIA algorithm utilizes the water wave optimization algorithm as its fundamental framework. The trained DQNAgent model acts as the decision maker, and together, they form the MODRL-SIA framework model, as illustrated in Figure 2. The algorithmic process of the MODRL-SIA framework is described in Algorithm 1. After being trained for a specific number of iterations, the MODRL-SIA framework algorithm can effectively address similar types of problems. It can serve as a versatile method for solving various combinatorial optimization problems.
which focus beams towards users [4]. The ability to direct signals toward specific areas and users has necessitated the development of new assessment methods to evaluate maximum exposure, as described in [5].  
This study presents the results of 400 in situ measurements taken outdoors at ground level across four distinct regions in Greece. The selection of measurement locations did not consider the proximity to cellular base station (BS) antennas, even though the density of BS in these areas was identified. Measurements were conducted without inducing additional traffic, using standard procedures to explore the impact of the 5G technology on the overall exposure. The first part of this research analyzed the data in relation to exposure limits and examined the contribution of cellular networks--from 2G to 5G--to the overall EMF levels. We also explored the specific impact of each cellular technology on the total field produced by cellular sources. The Kruskal-Wallis test [6] was applied to identify statistical differences between the recorded electric fields in different environments (urban/suburban). In the second part, we explored the correlation between EMF measurements in the 5G NR band at 3.5 GHz and their distance from 5G BS antennas, without causing traffic, which can be induced by using a smartphone. The studies of different propagation models [7] show that the signal from BS attenuates with distance. For that reason, the distance from BS is a significant input for artificial intelligence (AI) models aiming to predict the electric field levels [8; 9]. Regardless of traffic load, 2G, 3G, and 4G BS antennas maintain a nearly stable radiation pattern. In contrast, 5G BS antennas primarily broadcast signals which are usually transmitted periodically in varying directions. It is of interest to explore whether the variable radiation pattern of a 5G BS, without inducing traffic toward the measurement location, also leads to an inverse correlation between the recorded electric field strength and distance.
## 5 Discussion
### Orthogonal Questions  
_RQ-1I: What was the distribution of studies in terms of maintenance type, study type, and the algorithms used?_  
There are very particular phenomena that can be concluded upon after our work (see Figure 17). First, 63.57% of the studies worked with a preventive approach (with respect to the type of maintenance), and the second approach was corrective maintenance (29.9%). The rest of the alternatives had rates that were very far below the average.  
If we observe the types of ML algorithms that were used, they highlighted, in the first place, those related to artificial neural networks (ANNs), followed by support vector machines (SVMs) and random forests. This behavior is noted regardless of the type of software maintenance studied. With respect to the research mechanism, almost half of the studies opted for the proposal of new mechanics, but these, however, were not necessarily accompanied by robust methodological or evaluative processes to achieve their replication.  
_RQ-12: What was the distribution of studies in terms of software metrics, contributions of the articles, and types of ML algorithms?_  
Of all the \(11\) software metrics in the evaluated articles, a predominance of code smells as metrics linked to software maintenance was noted in 27 articles. Additionally, this option concentrated on the use of the supervised learning type of ML algorithm, capturing almost the entirety of this metric. Second, the KLOC metric was noted; again, it was measured predominantly through supervised learning. In total, the supervised learning approach stood out in 85.6% of the total results, regardless of the metric evaluated.  
Figure 17: Distribution of studies by type of maintenance, research, and ML algorithm.  
With respect to the contributions, half of the articles suggested support or prediction strategies for maintenance processes using ML. However, 28.93% suggested relative findings. These are reproducible under certain conditions but not necessarily replicable (see Figure 18).
# Tie-System Calibration for the Experimental Setup of Large Deployable Reflectors
## 2 Experimental Settings for Calibration  
Once the antenna has been manufactured, it is necessary to carry out some experimental tests in order to check the RMS of the reflector in the deployed configuration. To do this, the first operation consists of measuring the position of all nodes of the nets with respect to a reference system through one of the methods described above. Due to different sources of errors, such as manufacturing errors  
Figure 3: Laser tracker.  
or assembly errors, the deployed configuration will be different from the design configuration and the RMS will usually be greater than the design requirement. Even the measurement operation of the node co-ordinates will be affected by errors. Laser trackers and photogrammetry have errors on the order of 1 \(\upmu\)m; while laser radar reaches 0.1 \(\upmu\)m. Errors related to the measurement systems are, regardless, significantly below the preceeding mechanical errors. In the following, two methods for the experimental setting of the rigid and flexible ring truss supports are described.  
Several experimental methodologies [17; 18; 19; 20] based on a multi-body approach have been developed [21; 22; 23; 24; 25; 26; 27; 28; 29]. Recently, methods based on fuzzy logic [30; 31; 32; 33; 34], neural networks [35], and genetic algorithms have been applied for the tensioning of space trusses [36; 37]. The proposed methods act on the tensioning system of the ties in order to fix the errors coming from the construction of the antenna.
## Appendix B Formulas for R, K, S, L, and C  
R was calculated using precipitation data from 12 months and the year's average.  
\[\text{R}=\sum_{\text{i=1}}^{12}1.735\times 10^{[1.5\times\log{(\frac{\text{P}_{ \text{i}}^{2}}{\text{P}_{\text{year}}^{2}})}-0.8188]} \tag{12}\]  
where \(\text{P}_{\text{i}}\) is the monthly average precipitation, and \(\text{P}_{\text{year}}\) is the yearly average precipitation. K was calculated using an EPIC model using Equations (11)-(12).  
\[\text{K}_{\text{EPIC}}=\left\{0.2+0.3\text{exp}\Big{[}-0.0256\text{S}_{\text {a}}\Big{(}1-\frac{\text{S}_{\text{i}}}{100}\Big{)}\Big{]}\underset{\text{ 0.75}\text{S}_{\text{a}}}{\times}\Big{(}\frac{\text{S}_{\text{i}}}{\text{C}_{ \text{i}}+\text{S}_{\text{i}}}\Big{)}^{0.3}\times\Big{[}1.0-\frac{0.25\text{C }}{\text{C}+\text{exp}(3.72-2.95\text{C})}\Big{]}\times[1.0-\right. \tag{13}\]  
\[\text{K}_{\text{china}}=-0.01383+0.51575\text{K}_{\text{EPIC}}, \tag{14}\]  
\[\text{K}=0.1317\times\text{K}_{\text{china}}, \tag{15}\]  
where \(\text{K}_{\text{EPIC}}\) is the calculated American standard soil erodibility factor. \(\text{K}_{\text{china}}\) is the China standard soil erodibility factor corrected based on measured data. C is the soil organic carbon content. In Equation (13), \(\text{S}_{\text{a}}\), \(\text{S}_{\text{i}}\), and \(\text{C}_{\text{i}}\) represent the soil sand content, soil silt content, and soil clay content.  
Equation (4) includes the terrain factors: the slope degree factor and the slope length factor. Slope lengths longer than 5 m are calculated using Equation (10). Slope lengths less than 5 m were calculated using Equation (11) ([PERSON] et al., 1987):  
\[\text{S}=\left\{\begin{array}{cc}10.8\text{sin}\left(\theta\right)+0.03\ \ \theta\leq 5^{\circ}\\ 16.8\text{sin}\left(\theta\right)-0.5\ \ 5^{\circ}<\theta<10^{\circ}\\ 21.9\text{sin}\left(\theta\right)-0.96\ \ \theta\geq 10^{\circ}\end{array},\right. \tag{16}\]  
\[\text{S}=3.0\ \text{sin}^{0.8}\left(\text{tan}^{-1}\,\theta\right)+0.56. \tag{17}\]  
The slope degree factor was calculated using Equation (12) ([PERSON] et al., 1994) [21].  
\[\text{L}=(\lambda/22.13)^{\text{m}}=\left\{\begin{array}{cc}\text{m}=0.2\ \theta<0.5^{\circ}\\ \text{m}=0.3\ \ \ 0.5^{\circ}\leq\theta<1.5^{\circ}\\ \text{m}=0.4\ 1.5^{\circ}\leq\theta<3^{\circ}\\ \text{m}=0.5\ \theta\geq 3^{\circ}\end{array}\right. \tag{18}\]  
Factor C in Equation (4) should consider crop growth status, and thus FVC was introduced into Equation (19).  
\[\text{C}=\left\{\begin{array}{cc}1&\text{f}_{\text{c}}=0\\ 0.6508-0.3436\text{log}\text{f}_{\text{c}}\ 0<\text{f}_{\text{c}}\leq 78.3 \%\\ 0&\text{f}_{\text{c}}>78.3\%\end{array}.\right. \tag{19}\]
## 6 Future Research Directions
### Security  
The research directions in cybersecurity shall focus on developing deep learning models with dynamic and adaptable characteristics to counter the rigorously growing attack scenarios and preparing up-to-date datasets characterizing real-world application data frames with balanced classes to validate these models as a possible solution to practical network attacks.
# Indoor WiFi-Beacon Dataset Construction Using Autonomous Low-Cost Robot for 3D Location Estimation
## 3 Overall System Architecture
### Robot Sub-System Description
#### 3.1.1 External Overview  
An overview of the robot can be seen in Figure 2b. It includes three NodeMCU boards mounted at selected heights on an antenna tower, as seen in Figure 2b. The WiFi-transceiver logic-board (NodeMCU) modules are connected to a dedicated 8 V power-supply battery pack, as seen in Figure 2b: 4 \(\times\) 4 V Li-ion batteries are used, each two in series, to provide the WiFi transceivers with the necessary power during the extended hours of operation (more than four hours of operation for each run of data collection). Four DC motors are each connected to an individual wheel for precise movements and a motor power-supply sub-circuit (L298N), which is connected to a separate power-supply battery pack (8 V input using 2 \(\times\) 4 V Li-ion batteries in series) mounted at the bottom of the robot to ensure stability by having a low center of gravity in addition to ensuring simple cable routing and an optimal board layout. Two separate power supplies are used in order to isolate the mechanical sub-system from the logic sub-systems. This isolates the logic boards from the electrical noise usually produced by mechanical sub-systems. The body of the robot mainly consists of synthetic polymers that impose minimum interference with the Wi-Fi transceivers. In the final revision of the robot, the Wi-Fi transceivers were mounted on a wooden pole for the same reason. In addition, the main control unit and the client transceiver are linked using shielded CAT 5 network cables to reduce interference. Those precautionary measures ensure minimum impact of the robot itself on the measured signal strength and related communications.  
Two infrared transmitters and sensing modules for navigation are connected to the front of the robot for navigation, as seen in Figure 2a. The robot uses infrared (IR) transmitters and sensors to sense the track on which is it intended to traverse. To guarantee accurate displacement, many experiments were carried out by investigating different factors, such as the velocity of the wheels, the relative rotation of the wheels, and tracking module calibrations.
# Fast Low-Precision Computer-Generated Holography on GPU
## 5 Discussion  
Fixed-point integer approximation algorithms can bring significant speed improvements to CGH applications. Because they are primarily used for visualization and because in holograms, the information is generally distributed over the entire hologram plane, there is much more tolerance for errors than for conventional natural image and video rendering, which can be leveraged for speed.  
Many candidate CGH algorithms exist with potential for acceleration, but many tend to be rather complex, requiring several different transforms and operations, such as multiple fast Fourier transforms, non-uniform resampling, LUT use and non-linear occlusion operations. We opted to accelerate the point-cloud CGH algorithm, due to its simplicity, low memory requirements and high parallelizability, making it highly compatible for (simpler) FPGAs and ASICs.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Algorithm** & **PSNR (dB)** & **Calculation Time (ms)** & **Speedup Factor** \\ \hline Reference method & / & 3330 & 1.0 \\ Triangular approximation & 34.58 & **1068** & **3.1** \\ Piecewise approximation & **51.19** & 2594 & 1.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the results for the computed holograms comparing the different proposed algorithms. The best results in each column are indicated with boldface.  
Figure 9: Various reconstructions of the “bi-plane” hologram, for the three algorithm versions, reconstructed at three different depths. The columns stand for the reference (**a,d,g**), triangular approximation (**b,e,h**) and piecewise approximation (**c,f,i**). The rows show the front (**a–c**), middle (**d–f**) and back (**g–i**) of the bi-plane in focus, respectively.  
The advantage of the CGH method's simplicity also has a drawback in its limited supported shading effects. Extensions of the method addressing the limitation bring increased complexity in the algorithms, making the use of specialized hardware systems more difficult. The main disadvantage of the proposed method is the quality loss w.r.t. reference point-cloud method, though this can be controlled by selecting the appropriate approximation and bit-width depending on the CGH application's needs. Moreover, it has to be noted that the phase packing phase takes a non-negligible time on a GPU, and SIMD instructions are noticeably slower than the conventional instructions in the current GPU models. The obtained relative speed ups may thus be increased further in the context of FPGA or ASIC systems, where there is more fine-grained control on the bit width of various variables, as well as broader possibilities for customized instructions and the reduction of power requirements.  
In future work, we aim to implement optimized implementations on FPGA or ASIC systems, as well as refine the algorithm by supporting more visual effects and further reducing calculation requirements.
## 2 Methods
### Adaptive Construction of Deep Sequence and Feature Correlation Graphs
To tackle this, we implemented an adaptive mechanism for constructing deep sequence correlation graphs and feature correlation graphs without preset thresholds, enabling the model to dynamically learn the graph structure during training. The core idea of constructing the deep sequence correlation graph \(G_{d}\) and the feature correlation graph \(G_{f}\) lies in creating corresponding graph adjacency matrices \(A_{d}\) and \(A_{f}\). Using a self-attention mechanism [30] to build the graph allows for the creation of an attention adjacency matrix, capturing the significance of the logging data. In the aggregation phase, the model relies on the attention adjacency matrix's weights over the traditional scaled adjacency matrix's coefficients, ensuring the prioritization of relevant and significant data. Furthermore, the self-attention mechanism can generate values for \(A_{d}\) and \(A_{f}\) that are between 0 and 1, which aids in avoiding gradient explosion and vanishing issues when dealing with automatically constructed graphs. The deep sequence adjacency matrix \(A_{d}\) derived from the attention mechanism is expressed by Equation (5), where SoftMax is an exponential normalization function that transforms input values into output probability distributions; the hyperbolic tangent function, or Tanh, serves as the activation function, and \(W_{d,1}\) and \(W_{d,2}\) represent the learnable weight matrices within the attention mechanism. \[A_{d}=\text{SoftMax}(W_{d,2}\cdot\tanh(W_{d,1}\cdot X_{i}^{T})), \tag{5}\]  
Similarly, the feature adjacency matrix \(A_{f}\) derived from the attention mechanism is given by Equation (6), with \(W_{f,1}\) and \(W_{f,2}\) representing the learnable weight matrices within the attention mechanism. \[A_{f}=\text{SoftMax}(W_{f,2}\cdot\tanh(W_{f,1}\cdot X_{i})), \tag{6}\]  
The attention adjacency matrices \(A_{d}\) and \(A_{f}\) are used to construct the deep sequence correlation graph \(G_{d}\) and the feature correlation graph \(G_{f}\), respectively. These adjacency matrices are then utilized for spectral graph convolution operations, which are subsequently applied to the prediction of logging porosity. Part (B) of Figure 1 depicts the complete process of adaptively constructing the deep sequence correlation graph and the feature correlation graph.
# Root-Crush Protection Design of Automotive Bodies Using Clustering and Pattern Recognition
## 2 Modeling and Finite Element Analysis  
### Simplified FE Modeling  
In this study, a simplified finite element (FE) model was created to optimize the BIW design process as shown in Figure 1, based on the open computer aided design (CAD) files of a 2012 Toyota Camry model [21]. Then, basic data were created by changing the design variables to apply machine-learning techniques such as clustering and pattern recognition with optimization [9]. The simplified BIW model consisted of the design variables shown in Table 2. To reduce the time spent on finite element analysis (FEA), all BIW elements were configured as beam elements. The roof panel was configured as a rigid body and connected to each part of the BIW. To simulate the roof impact caused by a rollover, a rigid body wall at an incline of 23\({}^{\circ}\) was made to collide obliquely with the upper part of the car at 700 mm/s, as shown in Figure 1. A boundary condition was configured in which the end of each pillar was completely fixed while the rigid body wall collided with the BIW [7; 9]. In addition, SAE-AISI 1045 carbon steel (S45C) material was used for each part that makes up the BIW, and ABAQUS commercial software was used for the FEA.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**System** & **Major Components in System** & **Mass Breakdown (\%)** \\ \hline Body-in-white & passenger compartment frame, cross and side beams, front-end structure, roof structure, panels & 23–28 \\ \hline Powertrain & engine, transmission, exhaust system, fuel tank & 24–28 \\ \hline Chassis & chassis, suspension, tires, wheels, steering, brakes & 22–27 \\ \hline Interior & seats, insulation, trim, airbags & 10–15 \\ \hline Closures & front and rear doors, hood, lift gate & 7–8 \\ \hline Miscellaneous & lightning, windows, glazing & 7–8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mass proportion of vehicle systems and components [1].  
After the basic simplified FE model was configured, the diameters of ten parts were set as design variables, as shown in Table 2. A total of 2000 models were created by changing the design variables; these models were used for analysis. In order to extract variables without any overlap in the design space during this process, the Latin hypercube sampling technique was used. In Latin hypercube sampling, each probability variable is divided into \(N\) ranges so that samples can be extracted from the overall sample space considered. Then, one sample is extracted from each interval so that \(N\) samples are extracted without overlap. It is a sampling technique that can consider all design spaces without repetition [22; 23; 24].
# Fault Diagnosis Method Based on Time Series in Autonomous Unmanned System
## 3 Core Idea
### Overview of Our Model  
To solve the problems mentioned above, we designed a multi-sensor fault diagnosis method based on time series, as shown in Figure 1.  
First, the time series data of the autonomous unmanned system are collected, and the time series is dimensionally reduced by the symbolic representation method. Second, the similarity calculation is performed on the symbolized time series. Third, based on the similarity calculation results, a clustering algorithm is selected to identify abnormal data. In the end, we provide answers to questions Q1-3. The key techniques are as follows:  
1. When extracting and classifying time series, the characteristics of multi-unmanned system time series are first analyzed: high dimensionality and unknown distribution. The high dimensionality determines that we must represent the time series symbolically in order to achieve the purpose of dimensionality reduction. In the choice of the time series symbolization method, this paper proposes a symbolic time series classification method based on Gaussian mixture clustering (in Section 4.1.).  
2. The measurement of time series similarity is the core of cluster-based fault diagnosis algorithms. Whether the two time series are similar mainly depends on whether their changing trends are consistent. However, time series has the characteristics of high dimension and many data types, which brings great inconvenience to the follow-up research. Therefore, for the measurement of time series similarity, the similarity measurement function is particularly important. In Section 4.2., we define a time series similarity measure based on an improved Markov chain.