esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Finite Element Model
### Material Models  
The steel material of the girders is modelled using an elastic isotropic material in the elastic range with an elastic modulus of 200 GPa and Poisson's ratio of 0.3 and a perfectly plastic isotropic material in the inelastic range. The yield strength of the steel material is 345 MPa. The deck concrete has 23.1 MPa compressive strength (Ministry of Transport of the People's Republic of China) [19; 22; 23]. An empirical stress-strain model for unconfined concrete proposed by [PERSON] is used for the uniaxial stress-strain relationship of concrete [24].  
The creep and shrinkage are included in the model based on the equations from Ministry of Transport of the People's Republic of China [25]. The shrinkage effect is applied to the models through temperature decrease in the concrete material. ANSYS does not have direct method to calculate the creep effect. It gives metal creep model to model the creep in concrete. There are 13 creep equations in ANSYS and one used often is as follows:  
\[\dot{\varepsilon_{\alpha}}=C_{1}\sigma^{C_{2}}\varepsilon_{\alpha}{}^{C_{3}} \mathrm{e}^{-C4/T} \tag{1}\]  
where, \(\varepsilon_{\alpha}\) is creep strain; \(\varepsilon_{\alpha}\) is creep variance ratio of time; \(\sigma\) is stress; T is absolute temperature; C1 through C4 is parameters to be calculated. Usually, there are two ways to simplify the equation. One is to assume that creep variance ratio of time is only related to stress with C2 = 1 and C3 = C4 = 0 (Method A). Thus the equations is simplified as follows:  
\[\Delta\varepsilon_{\alpha}=C_{1}\sigma\Delta t \tag{2}\]  
For concrete with constant stress, the creep strains satisfy:  
Figure 3: Finite element model of a twin-I girder bridge with precast concrete deck.  
\[\varepsilon_{\rm cr}=\varepsilon_{0}\phi(t,t_{0})=\frac{\sigma_{0}}{E}\phi(t,t_{0}) \tag{3}\]  
Equation (3) is changed with time of \(\Delta t\):  
\[\frac{\Delta\varepsilon_{\rm cr}}{\Delta t}=\varepsilon_{0}\phi(t,t_{0})=\frac {\sigma_{0}}{E}\frac{\Delta\phi(t,t_{0})}{\Delta t} \tag{4}\]  
Within the time of \(\Delta t\), C1 is calculated as follows:  
\[C_{i}=\frac{\Delta\phi(t,t_{i-1})}{\Delta t}\frac{1}{E} \tag{5}\]  
Another way is assuming there is linear relationship between creep variation rate and strain with C2 = C4 = 0, C3 = 1 (Method B), which is denoted as follows:  
\[\Delta\varepsilon_{\rm cr}=C_{1}\varepsilon\Delta t \tag{6}\]  
For concrete with constant stress, the creep strains satisfy:  
\[\varepsilon(t)=\varepsilon_{\rm e}+\varepsilon_{\rm c}(t)=(1+\phi(t,t_{0})) \varepsilon_{\rm e} \tag{7}\]  
Thus within the time of \(\Delta t\), C1 is calculated as follows:  
\[C1=\frac{\Delta\phi(t_{i},t_{i-1})}{\Delta t(1+\phi(t_{i},t_{i-1}))} \tag{8}\]  
Figure 4 presents the validation of these two ways to analyse the creep effect for a column applied with a vertical constant force (denoted as \(P\)) and shows that the ways agree with the results using theoretical analysis.  
Figure 4: Finite element analysis using metal creep model for creep effect: (**a**) Method A; (**b**) Method B.
## 3 Results  
In Table 2, we present the descriptive statistics of the questionnaire factors. In particular, mean scores above 61% of the highest score for each factor were considered high, scores between 31% and 60% were considered average, and scores below 30% were considered low. Athletes exhibited average levels of \"Re-injury worry due to rehabilitation\" (M = 17.86 \(\pm\) 10.11, 31.89% of the highest score), \"Re-injury worry due to opponent's ability\" (M = 10.04 \(\pm\) 5.96, 35.85% of the highest score), and \"Distraction attention\" (M = 7.32 \(\pm\) 4.07, 34.85% of the highest score) (Table 2). Conversely, athletes demonstrated high levels of \"Confidence due to rehabilitation\" (M = 40.93 \(\pm\) 9.24, 73.08% of the highest score), \"General confidence\" (M = 32.32 \(\pm\) 7.52, 76.95% of the highest score), and \"Functional attention\" (M = 35.75 \(\pm\) 9.20, 72.95% of the highest score) (Table 2). The correlations between the three questionnaires and between the three functional tests are shown in Tables 3 and 4, respectively. In particular, Distraction Attention was negatively correlated with \"Functional attention\" (r = \(-0.55\), \(p<0.01\)), and \"Confidence due to rehabilitation\" was positively correlated with \"General confidence\" (r = \(0.56\), \(p<0.01\)) (Table 3). Many average and highly statistically significant correlations were found between the three functional tests (Table 4). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Questionnaire Factors** & **Score Range** & **M** & **SD** & **Minimum** & **Maximum** & **Skewness** & **Kurtosis** & **Percentage \({}^{*}\)** \\ \hline Re-injury worry due to rehabilitation & 8–56 & 17.80 & 10.10 & 8 & 56 & 1.23 & 0.50 & 31.80 \\ Re-injury worry due to opponent’s ability & 4–28 & 10.00 & 5.90 & 4 & 28 & 1.30 & 1.75 & 35.80 \\ Confidence due to rehabilitation & 8–56 & 40.90 & 9.20 & 15 & 56 & \(-0.80\) & 0.50 & 73.00 \\ General confidence & 6–42 & 32.30 & 7.50 & 10 & 42 & 1.23 & 1.20 & 76.90 \\ Functional Attention & 7–49 & 35.70 & 9.20 & 15 & 49 & \(-0.06\) & \(-0.50\) & 72.90 \\ Distraction Attention & 3–21 & 7.30 & 4.00 & 3 & 21 & 0.81 & \(-0.30\) & 34.80 \\ \hline \end{tabular} \({}^{*}\) Percentage of the mean score and the highest value of each factor.  
\end{table} 
Table 2: Descriptive statistics of the questionnaire factors.
# Toward Generative AI-Based Intrusion Detection Systems for the Internet of Vehicles (IoV)
## 1 Introduction  
The Internet of Vehicles represents a network that links vehicles, users, and intelligent devices to the Internet. It expands the Internet of Things (IoT) into advanced transportation by implementing next-generation technologies and employing mobile vehicles as carriers for information perception. The IoV allows vehicles to access resources such as cloud storage and computing, aiming to increase transportation efficiency and optimize the driving experience [1].  
As the Internet of Vehicles continues to expand, it is transforming transportation through seamless connectivity between vehicles, infrastructure, and cloud systems. However, this increased interconnectivity also introduces significant security challenges, making IoV networks a prime target for cyber threats. To mitigate these risks, Intrusion Detection Systems powered by ML and DL have been developed to detect and counter cyberattacks.  
These AI-driven IDS analyze vast amounts of network data to identify malicious activities and safeguard vehicle communication.  
Despite their advancements, ML- and DL-based IDS face key challenges, including high false positive rates, difficulty adapting to evolving attack strategies, and computational inefficiencies in real-time detection. Traditional models often struggle to capture complex attack patterns over time due to limitations in sequential data processing. Moreover, centralized IDS architectures pose scalability and security concerns, making them less effective in large-scale IoV environments.  
To address these limitations, researchers are turning to Generative AI-based IDS, which offer enhanced capabilities in detecting and responding to cyber threats. Generative models, such as Transformers and Large Language Models (LLMs), excel in analyzing sequential data and capturing temporal correlations in network traffic. These models improve IoV security by generating realistic attack scenarios for robust training, reducing false alarms, and adapting to emerging threats with greater precision. As a result, Generative AI provides a promising alternative to conventional IDS, paving the way for more resilient and intelligent cybersecurity solutions in IoV networks.
## 1 Introduction
### Conceptualization and Terminology  
[PERSON] and [PERSON] [4], who coined the concept, define AR as a technology that enhances the user's field of vision with the information necessary to perform a task, thanks to computational processes that can transform and chart simple graphics in real time. [PERSON] and [PERSON] [5] add to this definition when they describe AR as any case in which a real environment is enhanced with virtual objects (computer graphics). Likewise, they present the term within a taxonomy (a virtuality continuum), in which all the possible types of viewing appear. Within this continuum, the real and virtual elements coexist in a single mixed reality space where AR is closer to the entirely real environment than to the entirely virtual environment. Another pioneer, [PERSON] [6], sees AR as a variation of virtual environments which enables the user to see reality through superimposed objects.  
It is noteworthy that some authors [7; 8] have contradicted the idea of presenting AR conceptually as a technology in the strict sense, since it can be based on technology or understood as a resource that can accompany technology or draw from it, which means that it is necessary to interpret it beyond this exclusively classificatory treatment. Hence, one approach to this issue could be that AR is an emerging technique which is mediated by technology and which enables the superimposition of virtual information on a real environment, thus facilitating access to the borders of mixed reality, which can be two- or three-dimensional.
## 2 The Selection of the Fault Feature
### Analysis of Internal Parameters of Photovoltaic Array Equivalent Circuit in Fault State
When there is an open-circuits photovoltaic module in the photovoltaic array, the \(I_{\mathrm{ph}}\), \(R_{\mathrm{s}}\), and \(R_{\mathrm{sh}}\) of its string are zero, and the internal parameters of photovoltaic array equivalent circuit are:  
\[I_{\mathrm{ph}}\)\({}^{\prime}=(N-z)\bullet I_{\mathrm{ph}} \tag{3}\]  
\[R_{\mathrm{s}}\)\({}^{\prime}=\frac{M}{N-z}\bullet R_{\mathrm{s}} \tag{4}\]  
\[R_{\mathrm{sh}}\)\({}^{\prime}=\frac{M}{N-z}\bullet R_{\mathrm{sh}} \tag{5}\]  
Of which, \(z\) is the number of photovoltaic strings that has open-circuits. In this case, the \(I_{\mathrm{ph}}\)\({}^{\prime}\) decreases, while \(R_{\mathrm{s}}\)\({}^{\prime}\) and \(R_{\mathrm{sh}}\)\({}^{\prime}\) increase. From the above analysis and the literature [19], we know that when a photovoltaic module in photovoltaic array is abnormally aging, so its \(I_{\mathrm{ph}}\), \(R_{\mathrm{sh}}\) decreases, \(R_{\mathrm{s}}\) increases, the \(I_{\mathrm{ph}}\)\({}^{\prime}\), \(R_{\mathrm{sh}}\)\({}^{\prime}\) of photovoltaic array equivalent circuit decreases, and \(R_{\mathrm{s}}\)\({}^{\prime}\) increases. Therefore, the \(I_{\mathrm{ph}}\)\({}^{\prime}\), \(R_{\mathrm{s}}\)\({}^{\prime}\), and \(R_{\mathrm{sh}}\)\({}^{\prime}\) of photovoltaic array equivalent circuit can be used as the parameters of the photovoltaic array fault diagnosis model, which can effectively identify the faults of short circuit, open circuit, and abnormal aging.
## 3 Review Results
### Unified Theory of Acceptance and Use of Technology (UTAUT) and Extensions
#### 3.2.2. Utaut2  
The UTAUT2, an extension of the Unified Theory of Acceptance and Use of Technology (UTAUT), incorporates additional constructs such as hedonic motivation, price value, and habit. These factors are designed to provide a deeper understanding of consumer use and have been applied to explore ChatGPT adoption in educational settings. For instance, hedonic motivation, which refers to the enjoyment or pleasure derived from using ChatGPT, has positively influenced users' behavioral intention to adopt the technology [30]. This suggests that, when users find ChatGPT engaging and enjoyable, they are likelier to develop a positive attitude toward its use. However, some studies have reported that hedonic motivation did not significantly impact adoption, indicating variability in its influence based on different user contexts [39]. This variability highlights the need to consider how individual preferences and contexts may affect the role of hedonic motivation in the adoption process, reflecting the complex interplay of the factors influencing technology use in educational settings.
## 3 Results and Discussion
### Impact of Moisture Content on Tar Density  
The study of tar protocol overcame the tar problem and included standardized methods for sampling and analyzing it from the biomass gasification process. The tar protocol guideline contributes to the alternatives of analyzing tar compounds and tar density. Gas chromatography was used to analyze the tar compounds and this is widely implemented in studying the biomass gasification process. Additionally, to analyze the tar density, the gravimetric method was used alongside the distillation and evaporation processes [26; 27]. The study utilized the distillation process to evaporate the solvent with a specific boiling point. In this case, a 3 grams mixture of tar sample and acetone was heated for 5 hours at a temperature of 65 \({}^{\circ}\)C until all the acetone evaporated. The boiling point of acetone is 56 \({}^{\circ}\)C which allowed the separation of the tar sample, and all of the acetone to be evaporated. A mass of pure tar residue remained, and this was defined as tar density.  
The total amount of tar in the syngas was determined using Equation (3) as follows:  
\[vol_{tar}=FR_{syngas}\cdot t_{syngas} \tag{3}\]  
where \(vol_{tar}\) is the volume of tar in the total amount of syngas in the sampling (L), \(FR_{syngas}\) is the flow rate of the syngas in the tar sampling (L/min) and \(t_{syngas}\) is the sampling time of the syngas (min).  
The tar density in the syngas can be determined using Equation (4):  
\[\rho_{tar}=\frac{m_{tar}}{vol_{tar}}\;1000 \tag{4}\]  
where \(\rho_{tar}\) is the tar density in the syngas (mg/m\({}^{3}\)), \(m_{tar}\) is the mass of tar in the syngas in the sampling (mg), and \(vol_{tar}\) is the volume of tar in the total amount of syngas in the sampling (L).  
Figure 6 shows the result of gravimetric analysis for wood pellets in the biomass gasification with a variation of moisture content. When the moisture content was 2 wt. %, the tar-density value reached 1243.6 mg/m\({}^{3}\). This value gradually increased when it was 4 wt. %, and the tar density in the syngas reached 1572.7 mg/m\({}^{3}\). When the moisture content was 6 wt. %, tar density reached 1600.8 mg/m\({}^{3}\). The moisture content increases proportionally, raising the tar density. This reduced the reaction zone temperature in the gasifier, leading to the deposition of tar content. Additionally, the lowest moisture content produced the lowest tar density. In contrast, the higher value of tar density influenced and led to the failure of the operation of the biomass gasification process.  
Figure 6: Tar density on air-flow rate at 80 L/min with variations of moisture content: 2 wt. %, 4 wt. %, and 6 wt. %.
# A Hardware-Efficient Vector Quantizer Based on Self-Organizing Map for High-Speed Image Compression
## 3 Proposed Vector Quantizer Based on SOM
### The Squared Difference Unit and Memory Block  
The structure of SDU is illustrated in Figure 2. It mainly consists of numerous arithmetic units such as subtractors and multipliers as well as some auxiliary registers. To improve the hardware efficiency, several multiplexers are inserted between the subtractors and multipliers. All of the multiplexers are controlled by an independent signal \"\(S_{1}\)\". In this way, the data flow can be easily reconstructed by changing the value of \"\(S_{1}\)\". As depicted in Figure 2, the SDU can output either \((x_{j}-w_{ij})^{2}\) or \(\alpha(x_{j}-w_{ij})\), according to the value of signal \"\(S_{1}\)\". Since \((x_{j}-w_{ij})^{2}\) and \(\alpha(x_{j}-w_{ij})\) are separately the basic elements for SED calculating and new weight vector updating, the SDU can be successfully reconfigured in both codebook learning mode and image encoding mode without dedicated circuits.  
As shown in Figure 2, the SDU can process a 16-dimensional vector in every clock. To enable our proposed vector quantizer to deal with high-dimensional vector, we apply a mechanism of partial vector-component storage (PVCS) in the codebook memory. Typically, the \(d\)-dimensional input vector and weight vector are distributed into 16 memory blocks, and each complete vector contains \(m\) partial vector, where \(m=\lceil d/16\rceil\). Once the address counter for the vector accessing equals to \(m\), a separation signal \"\(S_{SEP}\)\" is asserted, which implies that an individual vector has been accessed entirely.
## 3 Results
### Association of the TMEM16A Proteins, and MTL and SP Hormones in JYGCT-Induced ITR Increase  
The normal ITR was 51.3 \(\pm\) 2.6% and JYGCT increased the ITR (53.7 \(\pm\) 3.2% at 0.01 g/kg, 60.6 \(\pm\) 2.9% (\(p\) < 0.001) at 0.1 g/kg, and 64.8 \(\pm\) 2.0% (\(p\) < 0.0001) at 1 g/kg) (Figure 5A). Next, we generated the GMD mouse model. AA decreased the ITR (25.9 \(\pm\) 1.8% (\(p\) < 0.0001), Figure 5B). However, JYGCT at 0.01, 0.1, and 1 g/kg restored this response to 51.3 \(\pm\) 2.6% (\(p\) < 0.0001), 51.7 \(\pm\) 3.9% (\(p\) < 0.0001), and 58.3 \(\pm\) 1.7% (\(p\) < 0.0001), respectively (Figure 5B). The reactions in ICC are mainly produced through non-selective cation channels, i.e., TRPM7, TMEM16A or c-Kit in the murine small intestine [15, 22, 23, 24]. The effects of JYGCT on the TMEM16A, c-Kit or TRPM7 proteins were examined in the GI tract; it was observed that the expression of TMEM16A increased significantly after JYGCT treatment (Figure 5C(a,b)). However, TRPM7 and c-Kit were unchanged (Figure 5C(c,d)). In addition, the levels of MTL and SP were significantly elevated (Figure 5D(a,b)) but the levels of SS (Figure 5D(c)), and VIP (Figure 5D(d)) showed no changes after JYGCT administration. Thus, these results indicate that the JYGCT-induced GI motility increase depends on the activation of the TMEM16A and MTL and SP hormones.  
Figure 4: Effect of SQ-22536, ODQ, KT-5823 and intracellular Ca2+ flux on the pacemaker potential inhibition by JYGCT in the ICC. (**A**) Pretreatment with SQ-22536 blocked the pacemaker potential inhibition by JYGCT; (**B,C**) Pretreatment with ODQ or KT-5823 did not block the pacemaker potential inhibition by JYGCT; (**D,E**) Summary of the inhibitory frequency and amplitude effects of pretreatment with SQ-22536, ODQ, or KT-5823; (**F**) Under normal conditions, [Ca2+]i oscillations were induced and JYGCT blocked these [Ca2+]i oscillations. Bars represents the mean \(\pm\) SE. \(p\) < 0.0001. JYGCT: Jakyakgamcho-tang; CTRL: control; SQ; SQ-22536; ODQ: 1H-[1,2,4]oxadiazol[4,3-a]quinoxalin-1-one.
## 5 Application of Multilayer Architecture
It manages the distribution of the global model to clients, collects local updates, and performs federated averaging or other aggregation techniques. The module also handles encryption and secure communication protocols to protect the integrity and confidentiality of the data being transmitted. A data management module: The data management module pre-processes the data, distributes them to participants, and ensures data privacy. This module is crucial for handling the diverse and often sensitive nature of the data used in FL. It includes functionalities for data normalization, anonymization, and encryption to comply with privacy regulations and protect user information. The module also manages the allocation of data to ensure balanced and representative training across all participants, improving the robustness and fairness of the final model. This approach offers a flexible, scalable, and reliable solution to address the challenges of Federated Learning in dynamic and heterogeneous environments. The use of a cloud and container environment simplifies the management of the system and adapts it to different needs. With this approach, you can leverage the benefits of Federated Learning to develop effective, privacy-preserving machine learning models in complex, ever-changing  
Figure 1: Schema of implementation using k3s. The k3s worker can be any architecture compatible with Kubernetes, with heterogeneous architecture. For instance, in our implementation, k3s workers are ARM virtual machines running in a cloud provider (Oracle). environments. The integration of advanced container orchestration and cloud capabilities not only enhances the scalability and flexibility of the system but also ensures robustness and reliability, making it well-suited for a wide range of applications where data privacy and security are priorities.
## 1 Introduction
[10] developed a laser transmitter, utilizing two DBR lasers and a pulsed tapered semiconductor optical amplifier (TSOA) to generate a peak power of 10 W with a pulse width of 1000 ns at a repetition frequency of 10 kHz around 828 nm. [PERSON] et al. [11; 12; 13; 14] developed a DIAL system based on two single DBR lasers and a TSOA emitting at 830 nm, having a peak power of 5.6 W within 900 ns at 9 kHz. Common to these references [8; 9; 10; 11; 12; 13; 14; 15] is the use of diode laser transmitters having an optical peak power of about 5 to 10 W with a pulse width of 900 ns to 1000 ns at a repetition rate of 6 to 10 kHz. To overcome the necessity of using two diode lasers as seed sources, in this work, we tested the implementation of a dual wavelength Y-branch DFB-laser as the master oscillator (MO) in a master-oscillator power-amplifier (MOPA) configuration recently reported [16; 17] based on a continuous-wave (CW) operated MO and a pulsed-driven power amplifier (SA) with an integrated optical gate. At wavelengths of 1060 nm and 975 nm, optical peak powers of 16 W were obtained for pulse widths of \(\tau\) = 2 ns and \(\tau\) = 8 ns, respectively, at repetition rates of \(f\) = 25 kHz. In this paper, a Y-branch DFB RW laser [18] is used as the master oscillator. In contrast to Y-branch DBR RW lasers [19; 20], DFB lasers offer the chance for a fast fine-tuning of the emission wavelength directly by varying the current, whereas DBR lasers can be only tuned by changing the grating temperature. To demonstrate the potential suitability of the device for the measurement of water vapor using the DIAL technique, the laser was selected to emit in the 964 nm to 968 nm range. Although the wavelengths are in the range of water vapor rotational-vibrational bands [21], the experiments were more focused on the spectral properties and the electro-optical characterization of the MOPA device than on the concrete realization of a ready-to-use device for DIAL. The MOPA system presented in this work is based on a Y-branch DFB laser operated in CW mode and a pulse-driven two-section PA. Alternating dual wavelength operation was obtained between 964 nm and 968 nm with a measured spectral linewidth below 10 pm and a peak power of more than 10 W. Limited by the available electronics, which was directly connected to the power amplifier, a repetition rate of 25 kHz was used, and a pulse width of 8 ns was obtained. In what follows, the devices and the MOPA configuration are shortly described. Then, the experimental power-voltage-current characteristics and the spectral properties of the two branches of the Y-DFB laser as well as the temporal and spectral behavior of the output power of the MOPA system are discussed.