esa-sceva/satcom-chunk-collection huggingface dataset:
# Shock Initiation of a Satellite Tank under Debris Hypervelocity Impact
## 1 Introduction
Results showed that the generated shock wave in the TITANK test was sufficient to achieve hydrazine explosion. However, many kinetic parameters in SIN, TDL, and ZEUS codes are still unknown, a database containing kinetic parameters for various propellants needs to be established. In the present work, the critical initiation criteria of explosives are elaborated and Walker-Wasley criterion is selected to predict the shock initiation of the satellite tank. Then, the minimum power density of liquid hydrazine is determined based on the WSTF#HYP-2 test, SAIC test and the designed TITANK test, the expressions of shock wave pressure and pressure duration are constructed based on the one-dimensional wave theory, and the initiation criterion for the liquid hydrazine tank are established. Finally, numerical simulation and the initiation criterion are used to calculate the power density in the satellite tank under the debris impact at the velocity of 10 km/s. Numerical simulation of the debris hypervelocity impact on satellite tank pays attention to the contact definition of debris/tank and debris/hydrazine, to the mesh size of liquid hydrazine and to the constraints of satellite tank [9]. Moreover, numerical modelling of the satellite tank under hypervelocity impact can offer a robust support for the structural design. In the future, tests of debris hypervelocity impact on a satellite tank will be carried out and the simulation results can provide an expected reference.
# Semi-Direct Point-Line Visual Inertial Odometry for MAVs
## 4 Experimental Results and Analysis
### Computational Performance
#### 4.2.1 Average Time for Tracking  
Firstly the computational performances when tracking the image were analyzed to compare SDPL-VIO and PL-VIO, and the comparison results are shown in Table 2. PL-VIO simultaneously extracts point and line features for each frame, which is very time-consuming. SDPL-VIO uses the semi-direct method for tracking, and takes much less time than PL-VIO. This is because non-keyframes account for the majority of the tracked features and the system uses the direct method, with the advantage of rapidity when tracking non-keyframes in the front end, and has no need to extract image features and calculate descriptors. This strategy effectively reduces the average front-end tracking time. Taking V201 as an example, as shown in Figure 13, according to the keyframe selection strategy, the number of selected keyframes is 127, accounting for about 23% of the total frames, while 77% of the frames were selected to be non-keyframes. However, the time needed to track keyframes accounted for 69% of the total time, while the time needed to track non-keyframes accounted for only 31%. Although the tracking time was reduced, the accuracy of SDPL-VIO was not significantly reduced; this was still comparable with PL-VIO, and even higher in some environments, as shown in Section 4.1.
###### Abstract
[13; 14] employed a thin composite arch structure reinforcement approach using sprayed steel fiber-reinforced concrete light rail to integrate the reinforced body with the original lining, forming a unified structure with enhanced integrity. [PERSON] et al. [15] employed finite element analysis to evaluate the stress distribution within the secondary lining and the concrete arch, both prior to and following the application of shotcrete arch reinforcement. [PERSON] et al. [16] investigated the impact of steel fiber-reinforced concrete linings on controlling surrounding rock displacement, reducing lining pressure, and enhancing both the overall and local stability of the supporting structure. [PERSON] et al. [17] conducted research on the use and efficacy of steel fiber shotcrete in single-layer lining structures for tunnel construction. [PERSON] et al. [18] explored the mechanical properties of single-layer steel fiber shotcrete linings and their application in tunnel engineering. [PERSON] et al. [19] investigated the mix design and construction processes of steel fiber shotcrete. It is evident that many applications of steel fibers in tunnel research focus on their integration during the construction phase within the concrete lining; however, research on the reinforcement of damaged concrete linings using steel fiber-reinforced concrete arches is scarce. Steel fiber-reinforced concrete differs from ordinary concrete in that it possesses high strength, excellent crack resistance, and substantial resistance to external impacts [20; 21]. Numerous experiments have demonstrated that the addition of steel fibers significantly enhances the toughness of concrete under compression. When cracks form in concrete, the stirrup effect of steel fibers within a compressed concrete column becomes evident, causing the structure to crack without leading to fractures or significant damage. This effect helps maintain the integrity of the original structure [22; 23]. The application of steel fiber-reinforced concrete technology in tunnel construction enhances the safety and stability of tunnel structures. Research in this domain would strengthen the original lining's structural properties, significantly increase its load-bearing capacity, effectively prevent the propagation of secondary cracks, and enhance overall tunnel safety. This study aims to bridge this knowledge gap. Initially, a 1:2 scale test was used to compare and analyze the reinforcement performance of steel fiber concrete and traditional reinforced concrete. Subsequently, the reinforcement effects of steel fiber-reinforced concrete arches on 1/3 and 2/3 prefabricated crack linings were analyzed using numerical methods. Finally, a method involving steel fiber-reinforced concrete arches is proposed to enhance the safety of damaged linings.
## 2 Foundational Concepts and Technologies
### Large Language Models (LLMs)
#### 2.2.2 Architecture  
The core principle behind LLMs is the use of neural networks (NNs), specifically a type of network known as the transformer [19]. Transformers have revolutionized the way models process sequential data. Unlike traditional recurrent neural networks (RNNs) and long short-term memory networks (LSTMs), transformers can handle long-range dependencies more effectively, making them ideal for language tasks. Transformers utilize a mechanism called self-attention, which allows the model to weigh the importance of different words in a sentence when making predictions. This is crucial for understanding context, as the meaning of a word often depends on the surrounding words.  
The architecture of a transformer consists of an encoder and a decoder, as shown in Figure 6. The encoder processes the input text, and the decoder generates the output text [19]. In practice, many LLMs use only the decoder part (as in GPT models [20]) for tasks like text generation, while others use both encoder and decoder (as in Gemini) for tasks that require understanding and generating text [29]. The encoder's role is to process the input sequence and convert it into a series of continuous representations. It does this through multiple layers of self-attention and feedforward neural networks. Each layer processes the input with a self-attention mechanism that allows the model to focus on different parts of the input sequence. The decoder also consists of multiple layers of self-attention and feedforward neural networks [20]. It takes the encoded representation from the encoder and generates the output sequence, one token at a time. During training, the decoder uses teacher forcing, where the actual previous token is used as input to predict the next token. During inference, it uses its own previous predictions to generate the next token. The self-attention mechanism is the heart of the transformer architecture. It allows the model to consider all positions of the input sequence when generating a particular token in the output sequence. This is achieved by computing a weighted sum of the input representations, where the weights are determined by the similarity between the query and key vectors of the input tokens [18].  
The transformer architecture, consisting of an encoder and decoder, revolutionized sequential data processing in LLMs. Self-attention allows the model to weigh the importance of different input elements and handle long-range dependencies effectively. This architecture enables LLMs to process and generate natural language with unprecedented proficiency, opening up new possibilities in AI applications.  
Figure 6: Transformer architecture, reprinted from Ref. [30].
These circuits allow the slope of the current-voltage characteristics in the NDR region to be controlled by changing the number of mirrored currents, thus allowing the peak to valley current ratio to be set to any desired value. Considering the importance and vulnerability of airborne equipment, high-altitude electromagnetic pulse (HEMP) protection of airborne equipment is necessary. In this context, [PERSON] et al. in [12] studied the characteristics of the HEMP radiation field on a typical transport aircraft using the frequency sweep method. The authors typically calculated and analyzed the electric field strength distributions of three layers (top, inner, and bottom layers). The achieved results have shown that the HEMP-induced field is impacted by the aircraft body. It varies greatly at different positions. Such a study can provide guidance and insights into the design of aircraft HEMP protection. In the final paper of this Special Issue [13], [PERSON] et al. addressed the important topic of energy harvesting by studying the performance of different piezoelectric-based transducers. In this context, a prototype has been proposed for the generation of energy from people's footsteps on different platforms based on macro-fiber composites and unimorph lead zirconate titanate transducers. The papers invited to contribute to this Special Issue are worthy examples of the breadth and scope of the topics published in the Electrical, Electronics, and Communications Engineering section of the Applied Sciences journal.
# Lightweight Integrity Preserving Scheme for Secure Data Exchange in Cloud-Based IoT Systems
## 3 Problem Statement
### Design Objectives  
In order to address the previously discussed issues, the system employed within this paper attains several safeguarding and execution assurances. For example:  
* Public integrity checking enables a semi-trust server to confirm the validity of information stored on the IoT-cloud system as requested, and does not require that they access a replica of the information in its entirety or partially, or have any further online consequences for smart device users.
* Storage suitability guarantees that the IoT-cloud system is not contamination by revealing positive results in the semi-trust server's audit without storing the smart device users' information.
* Additionally, bath integrity checking helps a semi-trust server that has confident and positive integrity checking abilities to handle numerous integrity checking assignments at the same time that are likely from numerous and varied smart device users.
* Semi-trust servers are lightweight, which helps them to complete audits with the lowest level of discussion or calculation expenses.
# Nutritional Status Disorders and Selected Risk Factors of
###### Abstract  
Introduction: The development of pneumonia in patients treated in intensive care wards is influenced by numerous factors resulting from the primary health condition and co-morbidities. The aim of this study is the determination of the correlation between nutritional status disorders and selected risk factors (type of injury, epidemiological factors, mortality risk, inflammation parameters, age, and gender) and the time of pneumonia occurrence in patients mechanically ventilated in intensive care wards. Material and method: The study included 121 patients with injuries treated in the intensive care ward who had been diagnosed with pneumonia related to mechanical ventilation. The data were collected using the method of retrospective analysis of patients' medical records available in the electronic system. Results: Ventilator-associated pneumonia (VAP) occurred more frequently in patients over 61 years of age (40.4%), men (67.8%), after multiple-organ injury (45.5%), and those with a lower albumin level (86%), higher CRP values (83.5%), and leukocytes (68.6%). The risk of under-nutrition assessed with the NRS-2002 system was confirmed in the whole study group. The statistical analysis demonstrated a correlation between the leukocytes level (\(p\) = 0.012) and epidemiological factors (\(p\) = 0.035) and the VAP contraction time. Patients infected with Staphylococcus aureus had 4% of odds for the development of late VAP in comparison to a Ciatheobacter baumannii (\(p\) < 0.001), whereas patients infected by any other bacteria or fungi had about four times lower odds of the development of late VAP in comparison to acinetobacter baumanni (\(p\) = 0.02). Patients with results in APACHE from 20 to 24 and from 25 to 29 had 13% and 21%, respectively, odds of the development of late VAP in comparison to patients with APACHE II scores ranging from 10 to 19 (respectively, \(p\) = 0.006; \(p\) = 0.028). Conclusions: The development of VAP is impacted by many factors, the monitoring of which has to be included in prophylactics and treatment.  
VAP; ventilator-associated pneumonia; risk factors; nutritional status disorders; intensive care ital
# The Effect of Insertion Angles and Depths of Dental Implant on the Initial Stability
## 3 Results
### Differences of PTV and ISQ among the Three Inserted Depth  
Regarding the effect of insertion depth on the fixtures on the PTV (Figure 5a,b), the implant PTVs measured along the mesial direction for the groups with 17\({}^{\circ}\) tilted implants were examined using the PTV of Group 1 as a reference. The PTV of the vertical evencrestal position and distal subcrestal position did not differ significantly; however, the PTVs were both significantly lower than those at the mesial supracrestal position. For the measurement results of the PTV at the distal direction, the implant at the vertical evencrestal position exhibited a lower PTV compared with that at the distal subcrestal position and mesial supracrestal position. The implant PTV between the distal subcrestal position and mesial supracrestal position exhibited no significant difference. Regarding the mean of the buccal and lingual directions group, the implant PTV at the mesial supracrestal position was higher than that at the distal subcrestal position and vertical evencrestal position. The distal subcrestal position also yielded a higher implant PTV compared with the vertical evencrestal position. Similarly, the implant PTV of the groups with 30\({}^{\circ}\) tilted implants measured along the mesial direction were examined using the PTV of Group 1 as a reference. Group 1 and Group 4 did not exhibit any significant difference in their implant PTVs from all directions, whereas both Groups 1 and 4 yielded greater PTVs than Group 5 from all directions.  
Regarding the effect of three insertion depths of the implant on ISQ (Figure 5c,d), because the ISQ was not affected by measurement direction, the mean of the four directions was used to represent  
Figure 5: PTV (**a,b**) and ISQ (**c,d**) measurements of Groups 1, 2 and 3 and Groups 1, 4 and 5.  
the effect of insertion angle on ISQ. Using the ISQ in Group 1 as a reference, for the groups with 17\({}^{\circ}\) tilted implants, the implant ISQ at the distal subcrestal position was significantly higher than that at the mesial supracrestal position. The ISQ at the vertical evencrestal position displayed no significant difference with those in Groups 2 and 3. Similarly, using the ISQ in Group 1 as a reference, for the groups with 30\({}^{\circ}\) tilted implants, the distal subcrestal position and vertical evencrestal position displayed no significant difference in their implant ISQ. However, the implant ISQ at the distal subcrestal position and vertical evencrestal position was significantly higher than that at the mesial supracrestal position.
# Dual-Frequency Microwave Plasma Source Based on Microwave Coaxial Transmission Line
## 2 Experiment Design
Reaction 7 is responsible for the heating of the gas. The 11.5 eV of energy, which was consumed in creating the electronically excited argon atom, returns to the gas as thermal energy when the excited metastable-state argon atoms quenching. In the simulation, the electron energy distribution function (EEDF) is approximately set as the Maxwellian distribution function, due to the fact that the discharge pressure 80 Pa is greater than 6.66 Pa [21, 22]. Although publications show that the plasma densities in simulations as Maxwellian distribution are higher than experimental measurements, they also show that the plasma distributions in simulations present good agreement with experimental measurements [23, 24, 25, 26]. Therefore, the Maxwellian distribution function is still useful, and the plasma distributions at different exciting powers are normalized, presented in Figure 2.  
As shown in Figure 2, the plasma discharge area is spread with increasing Port 1 microwave power. When the inputted 2450 MHz microwave power is at 500 W, the electron density near Port 2 is much lower than that near Port 1. When the inputted 2450 MHz microwave power is at 600 W, the electron density near Port 2 is a little lower than that near Port 1, but the plasma distribution is still non-uniform. When the inputted 2450 MHz microwave power is at 700 W, the electron density near Port 1 is nearly the same as that near Port 2, and in the area between Port 1 and Port 2, the plasma distribution is approximately uniform. While electron density is sufficiently affected by microwave power, the electron temperature is almost unaffected by microwave power changes. Therefore, the microwave power at Port 1 was set at 700 W to excite the plasma. \begin{table}
\begin{tabular}{c c c} \hline
**No.** & **Reaction** & **Type** & **Energy Loss \(\Delta\epsilon\) (eV)** \\ \hline
1 & \(e+Ar=e+Ar\) & Elastic \\
2 & \(e+Ar=e+Ar\) & Excitation & 11.5 \\
3 & \(e+Ars=e+Ar\) & Superelastic & \(-\)11.5 \\
4 & \(e+Ars=2e+Ar^{+}\) & Ionization & 15.8 \\
5 & \(e+Ar=2e+Ar^{+}\) & Ionization & 4.24 \\
6 & \(Ars+Ars=e+Ar+Ar^{+}\) & Penning ionization & \\
7 & \(Ars+Ar=Ar+Ar\) & Metastable quenching & \\ \hline \end{tabular}
\end{table} 
Table 1: Important collision processes in the argon discharge.
## 2 Background and Related Work  
### Service-Oriented Architectures  
In the SOA paradigm, distributed capabilities are organized by a unified concept called services. With the help of these, capabilities are made available and discoverable to each other. Therefore, service providers offer these services while service consumers are using them [18]. In the automotive setting, sensors, actuators, and computation entities provide and consume services. Computation can take place on an Electronic Control Units (ECUs) in the vehicle or on servers outside the car. The latter requires a reliable Wi-Fi or cellular network connection.  
Since the entities in the domains considered here are software/hardware systems, SOA is implemented through communication protocols. Thus, the protocol uniformly regulates how data on offering, discovery and interaction are exchanged via a network connection. In both cases, networks are increasingly based on Ethernet topologies. However, the protocols used in the automotive field (see Section 2.3) and medical environment (see Section 2.2) differ.  
In practice, most of the devices will provide as well as consume services, and therefore, a strict differentiation between both roles in a Operating Room (OR) setting and in the automotive domain will not be executed. Furthermore, both application domains of SOA have in common that the entities must meet special safety requirements, since malfunctions in the overarching system behavior could lead to life-threatening consequences.
## 3 Results
### Effect of Velocity Difference between a Leader and Main Group for Group Separation or Recombination  
For further consideration, the relationship between the velocity difference \(\Delta\)V between the leader and the main group and the state transition, considered to affect the separation and recombination of the group, was analyzed. The horizontal axis is V\({}_{lender}\), and the vertical axis is \(\Delta\)V, and they are plotted separately as the cases of transition from STR to STR and DIV (Figure 3a) and from DIV to STR and DIV (Figure 3b).  
When transitioning from STR to DIV compared to when the group stayed in STR (STR to STR), it was distributed in the upper right corner of the area partitioned by V\({}_{lender}\) = 14 m/s, \(\Delta\)V = 0 (Figure 3a). In contrast, when the group recombines from DIV to STR, it is distributed below the area partitioned by V\({}_{lender}\) = 14 m/s, \(\Delta\)V = 0 compared to when the group stayed in DIV (Figure 3b). The MANOVA results for V\({}_{lender}\) and \(\Delta\)V showed significant differences for STR to STR or DIV ([PERSON] \(\Lambda\) = 0.718 (F(2, 104) = 9.361, \(p\) = 1.828 \(\times\) 10\({}^{-4}\), \(\eta^{2}\) = 0.282)) and for DIV to DIV or STR ([PERSON] \(\Lambda\) = 0.848 (F(2, 108) = 4.632, \(p\) = 0.012, \(\eta^{2}\) = 0.152)), respectively. In some cases, it remained stretched with a relatively high \(\Delta\)V of 2.0 m/s (Figure 3a). In this situation, the group was on the verge of separating and might transition to a divided state at the next measurement point. Even in the stretched peloton configuration state, the group defined as \"group definition\" was formed. Therefore, even if the distance between the leading group and the main group widened due to the large \(\Delta\)V, it might remain in a stretched state with the presence of a small chasing group in front of the main group.  
This suggests that the combination of V\({}_{lender}\) and \(\Delta\)V was regarded as the descriptive parameter for state transition between STR to DIV and DIV to STR; however, each V\({}_{lender}\) and \(\Delta\)V was not considered as the descriptive parameter independently.