esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Implementation of Structural Reliability Assessment on the OWT Support Structure
### Constraints and Design Criteria  
In the design of OWT support structures, three limit states should be considered according to DNV-OS-J101, i.e., (1) ULS (ultimate limit state), which accounts for the maximum load-carrying capacity (i.e., buckling and yielding stress), (2) FLS (fatigue limit state), which accounts for failure as a result of cyclic loads, and (3) SLS (serviceability limit state), which accounts for tolerance criteria (i.e., vibrations and deflections) applicable to standard use. Thus, the structural reliability of OWT support structures in this study takes into consideration five design criteria, i.e., stress (ULS), vibration (SLS), buckling (ULS), deformation (SLS), and fatigue (FLS).
# LoRA-Tuned Multimodal RAG System for Technical Manual QA: A Case Study on Hyundai Staria
###### Abstract  
This study develops a domain-adaptive multimodal RAG (Retrieval-Augmented Generation) system to improve the accuracy and efficiency of technical question answering based on large-scale structured manuals. Using Hyundai Staria maintenance documents as a case study, we extracted text and images from PDF manuals and constructed QA, RAG, and Multi-Turn datasets to reflect realistic troubleshooting scenarios. To overcome limitations of baseline RAG models, we proposed an enhanced architecture that incorporates sentence-level similarity annotations and parameter-efficient fine-tuning via LoRA (Low-Rank Adaptation) using the bLLossom-8B language model and BAAI-bge-m3 embedding model. Experimental results show that the proposed system achieved improvements of 3.0%p in BERTScore, 3.0%p in cosine similarity, and 18.0%p in ROUGE-L compared to existing RAG systems, with notable gains in image-guided response accuracy. A qualitative evaluation by 20 domain experts yielded an average satisfaction score of 4.4 out of 5. This study presents a practical and extensible AI framework for multimodal document understanding, with broad applicability across automotive, industrial, and defense-related technical documentation.  
multimodal RAG; technical documentation; question-answering system; LoRA-based fine-tuning; domain adaptation; AI for structured manuals 2020 IEEE International Conference on Robotics and Automation (ICRA), Vol., pp. 1-10, 2018-2020,  
These limitations are not confined to automotive domains. Similar difficulties arise across various technical fields--including aerospace, heavy machinery, and defense--where structured manuals are essential for operational safety and system maintenance. This highlights the need for intelligent and scalable question-answering systems that can interpret and contextualize multimodal manual content.  
Recent advances in Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) techniques offer promising solutions to these challenges [4, 5]. By retrieving semantically relevant information and incorporating it into response generation, RAG-based systems enable more accurate and context-aware support across diverse domains. This study explores such an approach using automotive data, with the methodology being extensible to other sectors, including military and industrial applications.
## 4 Results and Discussion
### Stability Analysis of Mining Slope
#### 4.3.3 Ground Investigation of UAV Remote Sensing Time Series Images  
Through the UAV low-altitude photogrammetry and field investigation, it was found that the main surface damage caused by mining was cracks, followed by caving. In the vicinity of the Luhu landslide, surface disasters were mainly concentrated at the top of the landslide, as shown in Figure 12a,b. In this area there were more than 10 cracks, collapse pits, and small cavings caused by surface tensile deformation, with a crack length of more than 20 m and a width of 5-50 cm, perpendicular to the slope direction. The bottom of the slope was affected by non-uniform moving compression, as shown in Figure 12d-f. The road had been uplifted by up to 1.2 m and there were several cavings identified at the foot of the slope, where more serious damage had occurred.  
In the area affected by mining, it was found that the number and length of surface cracks increased with the advance of the working face. Combined with the UAV time series image and the corresponding deformation degree, it was found that when \(\varepsilon>4\) mm/m (i.e., 16 d) a surface uplift deformation was produced, and when \(\varepsilon>7\) mm/m (after 36 d) a small caving was produced. As shown in Figure 13, when the deformation reached a maximum and remained constant (at the inflection point of the horizontal deformation curve), three cavings were produced near the foot of the slope. When the deformation decreased, the rate of field failure decreased.  
By studying the non-uniform deformation curve, it was found that the failure of the slope body was limited, with the surface failure concentrated in areas in the accelerated or reduced stages of deformation. The instability of the slope body was increased during these periods. At the same time, the area where non-uniform deformation increased was mainly in the influence zone ahead of the working face, with the deformation rate decreasing in the influence zone behind the working face. By studying the displacement field, it was found that the horizontal movement of the top of the slope was larger than that of the foot of the slope, and they were moving in the opposite directions, which had a large influence on the stability of the slope body. From the UAV's low-altitude remote sensing time series images, it was found that with the advance of the working face, the type of ground failure in the slope area was dominated by cracks, with the number, size, and length of cracks also increasing. The high-steep slope stability in the mine was influenced by the starting deformation (low stability),  
Figure 12: Landslide damage in Luhu village as a consequence of mining. (**a**) is near the west side of point M02; (**b**) is between points M01 and M02; (**c**) is at the foot of the slope, below point M02; (**d**) is at the toe of the slope body; (**e**,**f**) are located at the road of the slope foot, near the point M06.  
iso-accelerated deformation (increased stability), deformation deceleration (reduced stability), and deformation remaining unchanged (improved stability).
## 2 Theoretical Background
### EMD-Based Methods
#### 2.2.2 EEMD Method  
EEMD, an improved version of the EMD method [23], is characterized by being a noise-assisted method employed for decomposing the 1D signals in their fundamental components according to the following steps:  
Step 1. Create new 1D signals by combining the initial 1D signal, \(x(t)\), with white Gaussian noise series, \(n_{i}(t)\), for \(m\) trials as follows:  
\[x_{i}(t)=x(t)+n_{i}(t)\;for\;i=1,\;2,\;3,\;\ldots,\;m \tag{5}\]  
Step 2. Decompose the 1D signals created in step 1 by employing the original EMD method.  
Step 3. Estimate a true frequency band or IMF, identified by \(j\), as follows:  
\[EEMD_{j}=\frac{1}{m}\sum_{i=1}^{m}c_{ij}(t) \tag{6}\]  
where the frequency band or IMF, identified by \(j\) for the trial \(i\), is denoted by \(c_{ij}(t)\).
## 5 Development of Multi-Core Hardware Platform for Energy Conservation
### Concepts of Using the Multi-Core Hardware Technology to Conserve Energy  
Multi-core WSN node is the node on which two or more independent cores (microcontrollers) are integrated cooperatively. These cores can share the platform resources and cooperate with each other to improve the system performance. Most current WSN nodes are the single-core ones, including the Mica, the micaZ, the TelosB, the Imote, the BTnode and the EYE. However, the development of the WSN nodes trends towards the multi-core platforms. This is because some WSN challenges, such as reliability and context awareness, can be addressed better by using the multi-core platforms.  
The concept of using the multi-core technology to reduce energy consumption in the MIROS derives from the experimental results that different cores are energy efficient in executing different tasks. Commonly, the cores with powerful computation ability are more energy-efficient to execute the complicated tasks while the cores with the weak computation ability are more energy-efficient to execute the lightweight tasks. In Table 10, the energy consumption of executing different tasks on the different cores is experimented. From these experimental results, it is shown that the 32-bit ARM core is more efficient in executing the complicated task, e.g., signal processing, whereas the 8-bit AVR core is more efficient at executing the simple task, e.g., sensor data collection. Due to these features, a multi-core WSN node can be developed. On this node, different types of cores can be integrated. During the run-time of a given task, the core which is the most energy efficient to execute this task can be configured to be active while the other cores can be configured to be inactive (fall asleep or be powered off). The WSN node can thus adapt flexibly to the different contexts, and the energy resource can be utilized more efficiently.  
\begin{table}
\begin{tabular}{|c|c|c|c|c|c|c|} \hline \multirow{2}{*}{**Tasks for the Measurements (Voltage: 3 V)**} & \multicolumn{2}{c|}{**32-bit ARM AT91 SAM7 Sx**} & \multicolumn{2}{c|}{**8-bit AVR ATmega1281**} \\ \cline{2-7}  & Current (mA) & Time (ms) & Energy (mJ) & Current (mA) & Time (ms) & Energy (mJ) \\ \hline Temperature and light sensor & \multirow{2}{*}{22.1} & \multirow{2}{*}{896} & \multirow{2}{*}{59.4} & \multirow{2}{*}{15.9} & \multirow{2}{*}{900} & \multirow{2}{*}{42.9} \\ sampling task & & & & & & \\ \hline Signal processing task (pure & \multirow{2}{*}{19.7} & \multirow{2}{*}{5.0} & \multirow{2}{*}{0.296} & \multirow{2}{*}{9.9} & \multirow{2}{*}{268} & \multirow{2}{*}{7.96} \\ instruction execution) & & & & & & \\ \hline Flash programming (10-byte) & 20.9 & 6.0 & 0.376 & 16.3 & 10.1 & 0.49 \\ \hline Sleep & 0.2 & N/A & N/A & 40 uA & N/A & N/A \\ \hline \end{tabular}
\end{table}
Table 10: Energy consumed by executing different tasks on different cores.
# Cybersecurity and Medical Imaging: A Simulation-Based Approach to DICOM Communication
## 5 Simulation Results and Analysis  
The diagram in Figure 3 represents the simulated environment and the integration between the DICOM simulation and the PACS server. It comprises three key components: a Windows 10 machine on which the DICOM simulation is executed, an Ubuntu Virtual Machine (VM) with a Docker container as a deployment for Orthanc, and a dedicated KALI Linux machine to enable packet sniffing. The simulation and the deployed topology can be used as a sandbox for DICOM communication security scenarios.  
To ensure device identification within the local network (Figure 3), specific IP addresses are assigned for the VMs of Windows 10, Ubuntu VM, and KALI Linux. The reserved IP addresses facilitate communication and data exchange among the different components of the simulated environment.
# Wireless and Fiber-Based Post-Quantum-Cryptography-Secured IPsec Tunnel
## 3 IPsec Protocol
#### IPsec Hardware Acceleration
BlueField-2 DPUs use dedicated pipes to match packet headers against predefined criteria, such as 5-tuple (source/destination IP addresses, source/destination ports, and protocol). The 5-tuple includes the IP address of the device that sends the packet, as well as the IP address of the intended recipient device. Additionally, it includes the source port number used by the application to send data on the source device, as well as the destination port number used by the application to receive data on the destination device. Finally, it specifies the type of protocol used for communication, such as TCP or UDP. Once a packet is classified and matched, it undergoes encryption or decryption based on the established IPsec policies. BlueField-2 DPUs support hardware-accelerated encryption/decryption operations, leveraging dedicated cryptographic engines ([[https://docs.nvidia.com/doca/sdk/nvidia+doca+ipsec+security+gateway+application+guide](https://docs.nvidia.com/doca/sdk/nvidia+doca+ipsec+security+gateway+application+guide)]([https://docs.nvidia.com/doca/sdk/nvidia+doca+ipsec+security+gateway+application+guide](https://docs.nvidia.com/doca/sdk/nvidia+doca+ipsec+security+gateway+application+guide)) accessed on 10 January 2024) for fast and secure data processing. Figure 3: DOCA IPsec Flow diagram. Encrypted packets are encapsulated with additional IPsec headers before being forwarded to the appropriate destination. BlueField-2 DPUs handle encapsulation efficiently, ensuring minimal overhead and latency in the data transmission process. In the secure egress domain, BlueField-2 DPUs perform additional processing to ensure proper packet routing and security enforcement. This may involve IP classification, encryption pipe selection (IPv4 or IPv6), and the application of IPsec policies based on metadata matching. Metadata refer to additional information attached to a packet that can provide context beyond the basic header information. For IPsec, metadata might include details about the specific SA used for encryption, enabling the DPU to select the appropriate algorithms and keys. BlueField-2 DPUs seamlessly integrate with the application layer, providing APIs and interfaces for application developers to define and enforce IPsec policies, monitor network traffic, and manage security configurations. The hardware acceleration capabilities of BlueField-2 DPUs significantly enhance the performance and scalability of IPsec implementations in data center and cloud environments. By offloading intensive cryptographic operations to dedicated hardware, BlueField-2 DPUs optimize resource utilization and improve the overall system efficiency.
# Multi-Modality Cardiovascular Imaging Assessment in Fabry Disease
## 1 Introduction  
Fabry disease (FD) is an X-linked lysosomal storage disorder, which manifests as a deficiency in the enzyme \(\alpha\)-Galactosidase A [1]. This leads to progressive and multi-organ accumulation of sphingolipids, particularly globotriasylceramide (Gb3) and lyso-globotriasylceramide (lyso-Gb3) [2]. Over time, this results in end-organ damage with predominant cardiac, renal, and cerebrovascular manifestations that cause significant morbidity and premature mortality [3]. Enzyme replacement therapy (ERT) and oral chaperone therapy (OCT) may stabilise renal function and cardiomyopathy if initiated early in the disease process. Sphingolipid accumulation takes place in all cardiac cell types, leading to left ventricular hypertrophy (LVH), microvascular ischaemia, conduction abnormalities, congestive cardiac failure, and valvular disease. Prevalence is estimated to be between 1:40,000 and 1:117,000 live births. Although Fabry disease is rare, it serves as aparadigm for the application of modern cardiovascular imaging techniques in diagnosis, guiding therapy and determining prognosis. The aim of this article is to outline the advantages of state-of-the-art imaging in the staging, management, and prognosticating of patients with FD.
## 3 Results and Discussions
### Hydrogen/Air Mixture
#### 3.1.2 Time-Averaged Total Specific Impulse  
The time-averaged total specific impulse is reported in Figures 7-10 for all the cases that achieved stable detonation. As the specific impulse is defined based on thrust, the same operating regimes (i.e., above 6 bar oxidizer pressure) are plotted.  
For ignition frequencies of 100 Hz (Figure 7) and 150 Hz (Figure 8), the highest total specific impulse levels (in the range 1000-1600 s) are recorded for an exhaust pipe length of 400 mm. For 233 Hz (Figure 9) and 350 Hz (Figure 10) ignition frequency, the 200 mm configuration yields the maximum total specific impulse around 0.6 ER with 4.5 bar oxidizer pressure. However, sharp gradients in the vicinity of the maximum lead to low total specific impulse, almost half of the maximum. For 233 Hz (Figure 9) and 350 Hz (Figure 10) spark plug frequency, the 400 mm configuration results in a smoother total specific impulse trend with ER in the range 800-1200 s.  
Figure 5: Time-averaged thrust for a frequency of 233 Hz for \(H_{2}\)/**air**.  
Figure 6: Time-averaged thrust for a frequency of 350 Hz for \(H_{2}\)/**air**.  
Figure 8: Time-averaged total specific impulse for a frequency of 150 Hz for H\({}_{2}\)/air.  
Figure 7: Time-averaged total specific impulse for a frequency of 100 Hz for H\({}_{2}\)/air.  
Figure 9: Time-averaged total specific impulse for a frequency of 233 Hz for H\({}_{2}\)/air.  
For high oxidizer pressures (i.e., 6 bar), where less stable detonation regimes were achieved, the 400 mm configuration shows an increase in the total specific impulse with ER, for all spark-plug frequencies, except 233 Hz. Additionally, for 100 Hz (Figure 7) and 150 Hz (Figure 8), the 400 mm configuration yields a higher total specific impulse compared to the other configurations that perform below 600 s in terms of specific impulse.
## 1 Introduction
Therefore, this paper provides a detailed study on the green-water loads induced by the freak wave and the corresponding structural responses of the deck and breakwater structures of a ship bow. To achieve this, firstly, a three-dimensional numerical wave tank is established in which the superposition model is used to generate freak waves. Validations on the freak-wave generation, ship motion response and wave loading are carried out to verify the present issues. The simulation of the interaction between the freak wave and the ship are conducted to obtain the interaction process and green-water loads. Secondly, a finite element model of the ship bow is built, on which the green-water loads are applied to calculate the structural responses. Finally, the displacement and stress of the deck and breakwater structures are analyzed, while conclusions on the safety of the ship bow is drawn. The novelty of the study lies in that a comprehensive investigation of the ship's safety under freak-wave conditions are conducted, including the motion response, green-water impact loading and the structural response. More importantly, the structural responses and damages of the deck and breakwater structures during the green-water event induced by the realistic scale freak wave are examined for the first time, which provides a meaningful reference value for the practical engineering design of ship deck and breakwater structures.
# Deep Learning-Based Hip X-ray Image Analysis for Predicting Osteoporosis
## 4 Experimental Results
### Image Classification Results
#### 4.2.1 Categorization Results Using the Original Dataset
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{1}{c}{} & \multicolumn{6}{c}{**Femoral Neck**} \\ \hline
**Indicators** & \multicolumn{2}{c}{**Sensitivity**} & \multicolumn{2}{c}{**Specificity**} & \multicolumn{2}{c}{**F1-Score**} & \multicolumn{2}{c}{**Accuracy**} \\ \hline  & **Training** & **Testing** & **Training** & **Testing** & **Training** & **Testing** & **Training** & **Testing** \\ \hline VGG16 & 0.89 & 0.58 & 0.94 & 0.59 & 0.91 & 0.57 & 0.92 & 0.59 \\ \hline ResNet50 & 0.49 & 0.52 & 0.56 & 0.57 & 0.46 & 0.49 & 0.52 & 0.55 \\ \hline DenseNet121 & 0.80 & 0.42 & 0.93 & 0.65 & 0.86 & 0.46 & 0.88 & 0.55 \\ \hline  & \multicolumn{6}{c}{Greater Trochanter} \\ \hline VGG16 & 0.96 & 0.23 & 0.99 & 0.90 & 0.97 & 0.31 & 0.99 & 0.63 \\ \hline ResNet50 & 0.51 & 0.53 & 0.56 & 0.56 & 0.44 & 0.47 & 0.52 & 0.55 \\ \hline DenseNet121 & 0.42 & 0.33 & 0.80 & 0.72 & 0.48 & 0.35 & 0.65 & 0.57 \\ \hline  & \multicolumn{6}{c}{Ward’s Triangle} \\ \hline VGG16 & 0.99 & 0.84 & 0.94 & 0.16 & 0.99 & 0.74 & 0.98 & 0.61 \\ \hline ResNet50 & 0.72 & 0.63 & 0.35 & 0.34 & 0.70 & 0.61 & 0.61 & 0.53 \\ \hline DenseNet121 & 0.76 & 0.77 & 0.34 & 0.32 & 0.74 & 0.73 & 0.63 & 0.62 \\ \hline  & \multicolumn{6}{c}{Total Hip} \\ \hline VGG16 & 0.99 & 0.72 & 0.99 & 0.66 & 0.99 & 0.68 & 0.99 & 0.69 \\ \hline ResNet50 & 0.42 & 0.41 & 0.72 & 0.67 & 0.47 & 0.44 & 0.58 & 0.55 \\ \hline DenseNet121 & 0.51 & 0.54 & 0.49 & 0.46 & 0.43 & 0.44 & 0.50 & 0.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experimental results of image classification by parts (original dataset with T-score).
## 5 Discussion
For example, the 3D parts could be manufactured in cheaper polymers or metals, and then metallised afterwards with a good conductor layer. Manufacture characteristics of the 3D printed part can be seen in Figure 14. Thanks to the ESIW-to-3D waveguide transition presented in this article, some of the transition from planar lines to ESIW [16; 17; 18] can be used to connect these planar lines to 3D structures, such as 3D printed antennas [19], without the need of flanges, and with a good electrical performance. It allows us also to connect with different Ridge waveguides devices (antennas, dividers, etc.) [20; 21; 22; 23; 24; 25]. Additionally, this device can benefit from both manufacturing techniques SIC [26] and additive manufacturing [27; 28]. Figure 11: Transmission coefficient (S21) of various structures of SIW-to-3D waveguide transition: pins, substrate, u-shaped, and ideal. Figure 12: Transmission coefficient (S11) of various structures of SIW-to-3D waveguide transition: pins, substrate, u-shaped, and ideal. Figure 13: Parts of the prototype and integrated structure. The comparison with other transitions can be observed in Table 3, noting that the compared devices are quite different from the one proposed in this article since it is the first of its kind. Therefore, this comparison serves more as an overview of current transitions. The proposed transition is easy to manufacture and design, works with very low losses in the operation bandwidth, and connects two technologies that could not be connected easily until now. As we can observe, the proposed transition exhibits very minimal losses. In fact, it stands as the best in terms of losses. Furthermore, it seamlessly adapts to other frequencies by adjusting the values of \(a\) and \(b\) without the need for optimisation, in contrast to the other ones. In terms of bandwidth, it perfectly covers the entire bandwidth of the WR-62 standard for waveguides, and if necessary, it is possible to cover very wide frequency bands, for example by incorporating the ridge section.
## 3 Results and Discussion
### Investigation of 3D-rGOPFH
#### 3.1.2 Selection of 3D-rGOPFH  
To identify the optimal 3D-rGOPFH, eight 3D-rGOPFH with different loading amounts of PNT (20:1, 16:1, 12:1, 8:1, 4:1, 2:1, 1:1 and 1:2) were synthesized and compared. The effect of these sorbents on the extraction efficiency was investigated for the determination of thirteen insecticides. According to Figure S2, higher extraction efficiency was obtained when the ratio of the mass of PNT (m\({}_{\rm PNT}\)) to that of GO (m\({}_{\rm GO}\)) was 12:1. The extraction efficiency gradually decreased as the m\({}_{\rm PNT}\):m\({}_{\rm GO}\) ratio was changed from 12:1 to 1:2. When more amounts of PNT were introduced, the extraction efficiency was improved, owing to the filling effect and strong adsorption forces of PNT. As the PNT ratio (>12:1) was further increased, its adsorption efficiency decreased. An excessive PNT in the 3D-rGOPFH would lead to a greater difficulty in maintaining a fixed gel shape. The results indicated the deterioration of a 3D structure of 3D-rGOPFH might reduce extraction efficiency.