esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Numerical Model Calibration  
### Calibration Methodology  
The calibration of the numerical model against the experimentally obtained data was primarily based on HRR, with temperature serving as a secondary reference. Despite its  
Figure 14: Oxygen, carbon monoxide and dioxide concentration variation inside the stand.  
secondary role, temperature still provided essential insights into vertical gradients during key phases like flashover. This dual process is also aimed at validating the accuracy of the numerical model. To optimize the process of obtaining numerical data, a refinement analysis of the domain was conducted in the current phase to produce feasible results with minimal resource consumption. As a result, three known and independent input parameters were varied (as shown in Table 1), generating a total of eight simulations. Recognizing that the full experimental duration was approximately 16,000 s (5 h), the simulation was limited to 1280 s to capture the key phases of fire evolution effectively.  
To assess the accuracy of the simulations, two quantitative indicators were used: Mean Relative Error (MRE) and Root Mean Square Error (RMSE) over a relevant time interval, hereafter referred to as the \"useful interval\". The useful interval was defined as the period during which both experimental data and numerical results are relevant and comparable, capturing the key fire phases while maintaining computational efficiency. Its definition was necessary because the aim was not to conduct a time-intensive simulation equivalent in duration to the experiment. Given that the maximum temperature was recorded at T = 1234 s, it was deemed appropriate to limit the simulation duration to 1280 s. This allowed for the inclusion of all defining phases of fire evolution while ensuring a reasonable computation time for the simulations. The useful interval was established after excluding an initial period, spanning from T = 100 s to T = 1280 s, and was determined to be ample for optimal model calibration.  
For MRE, comparison will adhere to an acceptability threshold of \(\pm\)10% within the useful interval, with values outside this range being deemed unsatisfactory for validation purposes. For RMSE, a systematic approach was employed by constructing an error matrix (Figure 15) that combines three independent sets of factors, each with two variants (2 \(\times\) 2 \(\times\) 2): cell size, source intensity (HRRPUA), and type of domain discretization. Each combination will result in an RMSE value calculated over the same useful interval, with the configuration yielding the lowest error being considered the most precise and relevant model in terms of proximity to experimental reality. The objective in evaluating the numerical model's accuracy was to achieve an optimal balance between result precision, with RMSE minimized, MRE falling within a \(\pm\)10% margin, and computation time not exceeding 100 h (Table 1).
# Assessment of Modal Characteristics of Steel-Concrete Composite Grider Bridge with V-Shaped Piers
## 5 Analysis of Structural Self-Vibration Characteristics
### Influence of Main Beam Stiffness on Self- Vibration Characteristics
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Modal** & \multicolumn{5}{c}{**Self-Vibration Frequency**} \\
**Order** & \(-\)**10\%** & \(-\)**5\%** & **0** & **5\%** & **10\%** \\ \hline
1 & 2.04 & 2.03 & 2.03 & 2.03 & 2.03 \\
2 & 3.74 & 3.79 & 3.77 & 3.82 & 3.87 \\
3 & 4.84 & 4.86 & 4.84 & 4.86 & 4.88 \\
4 & 5.06 & 5.09 & 5.08 & 5.11 & 5.14 \\
5 & 5.29 & 5.37 & 5.36 & 5.43 & 5.49 \\
6 & 7.17 & 7.26 & 7.25 & 7.34 & 7.40 \\
7 & 7.84 & 7.96 & 7.90 & 8.02 & 8.10 \\
8 & 8.08 & 8.19 & 8.14 & 8.24 & 8.32 \\
9 & 8.81 & 8.80 & 8.79 & 8.78 & 8.77 \\
10 & 9.01 & 9.10 & 9.09 & 9.20 & 9.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Self-vibration frequencies (Hz) for different main beam stiffnesses of Structure II. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Modal** & \multicolumn{5}{c}{**Self-Vibration Frequency**} \\
**Order** & \(-\)**10\%** & \(-\)**5\%** & **0** & **5\%** & **10\%** \\ \hline
1 & 1.22 & 1.22 & 1.22 & 1.22 & 1.22 \\
2 & 1.40 & 1.42 & 1.43 & 1.46 & 1.47 \\
3 & 2.64 & 2.64 & 2.64 & 2.63 & 2.63 \\
4 & 3.31 & 3.37 & 3.38 & 3.44 & 3.49 \\
5 &The first two orders of the self-vibration frequencies of Structure II decrease as the stiffness of the main beam increases, while the self-vibration frequencies from the third to ninth order increase with the stiffness of the main beam, but to a smaller extent.
## 4 Discussion
### Real-World Insights and Challenges  
Deploying JAVIS in a hospital environment revealed significant insights. The strict approval processes required for enabling IP access initially delayed adoption but undersocored the necessity of LLMs in strictly offline settings, where internet access is unavailable or highly restricted. In such environments, LLMs became indispensable resources for addressing coding issues, solving EMR-related tasks, and providing quick access to critical knowledge. Physicians, in particular, benefited from the system's capacity to assist with clinical queries and memory recall, thereby enhancing productivity and informed decision-making across multiple departments.  
Despite the clear value offered by offline LLMs, user adaptation and staff training also emerged as key considerations during JAVIS's pilot deployments. Early prototypes presented usability challenges, including a chat interface that did not auto-scroll to new messages, a non-expandable input field, and a lack of clarity around API integration. These design limitations initially hindered seamless integration into day-to-day workflows and required additional support from IT teams. However, real-time feedback mechanisms led to continuous improvements: modern interfaces, intuitive drag-and-drop file upload, and clearer documentation for API usage. Consequently, new users now report minimal onboarding time, thanks to an interface that closely resembles common chat platforms.  
Formal training programs or lengthy onboarding manuals have proved largely unnecessary, reducing both the time and costs typically associated with user support. Most inquiries today focus on file format compatibility and specialized features rather than navigating the system itself. Nonetheless, IT support and concise documentation remain essential for advanced functionalities, such as custom integrations or specialized data parsing, which some researchers and clinicians require. By maintaining a user-centric design philosophy and accessible support resources, JAVIS ensures long-term adoption and sustained engagement across diverse hospital departments.
# Towards mmWave Altimetry for UAS: Exploring the Potential of 77 GHz Automotive Radars
## 5 Operational Requirements and Radar Specifications
### Range Resolution and Accuracy  
In the context of radar altimeters, the range resolution essentially translates to range accuracy since a finer range resolution allows for the more precise discrimination between altitude levels. If the range resolution is too coarse, it may lead to ambiguity in determining the actual altitude within a given range bin. The range accuracy requirement can be inferred conveniently from aviation standards [59; 60]. The requirement for range accuracy is \(\pm 1.5\) ft, which equates to a range resolution of 3 ft or 0.9 m. However, there is room for the lower limit of measurable altitude being 20 ft to be improved for the case of small-sized drones relying solely on a radar sensor for landing. Since most legacy radar altimeters operate between 4.2-4.4 GHz, there is a requirement to keep a significant separation between the transmit and receive antennae to avoid signal coupling. The mmWave length has an intrinsic benefit of a very small antenna separation requirement. This, coupled with the high range resolution, enables a significantly smaller minimum measurable altitude value. This feature may be particularly useful during the flight operation of mini-drones to aid in efficient traffic management within the scope of smart cities [63].  
Figure 7: Rationale for update rate requirement.
## 4 Experiments and Analysis of Results
### Case Study 1: CWRU Dataset
#### 4.4.2 Fault Classification Model Evaluation
experiments are evaluated using four metrics: accuracy, precision, recall, and F1 score, calculated as follows:  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{28}\]  
\[\text{F1}=\frac{2\cdot\text{Precision}\cdot\text{Recall}}{\text{Precision}+\text{ Recall}} \tag{29}\]  
Table 7 shows that Random Forest and SVM have comparable performance in traditional machine learning methods. Among deep learning methods, hierarchical CNN performs better in F1. However, the classification performance of 2D-CNN and 2D-ResNet is low, especially 2D-ResNet, with an accuracy of 92.54 and an F1 score of 93.06. In contrast, the proposed method in this paper has significant advantages in all evaluation metrics and outperforms other models in all four metrics. These results indicate that a classification model utilizing data generated by MDFT-GAN can more effectively capture data characteristics, delivering superior performance in both classification accuracy and stability.
# Cooperative Jamming-Based Physical-Layer Group Secret and Private Key Generation
## Appendix A Proof of Theorem 1
### The SK Lower and Upper Bounds  
To derive \(R_{s}\), \(I(\tilde{Y}_{A};\hat{G}_{1,B})\) in (19) is approximated as  
\[I(\tilde{Y}_{A};\hat{G}_{1,B})=\log_{2}\left(1+\frac{p_{A}^{2}}{3\mu^{-1}p_{A }+2\mu^{-2}}\right)\approx\log_{2}\frac{\mu p_{A}}{3}. \tag{32}\]  
Since \(\sigma_{1}^{2}=\sigma_{2}^{2}=\sigma_{3}^{2}\), it can be observed that \(I(\tilde{Y}_{A};\hat{G}_{1,C})=I(\tilde{Y}_{A};\hat{G}_{1,B})\) by comparing (19) and (20). Therefore, based on the expression of \(R_{s}\) in (14), we can further infer that in the high SNR regime,  
\[R_{s}\approx\log_{2}\frac{\mu p_{A}}{3}. \tag{33}\]  
For the derivation of \(C_{s}^{U}\), \(I\left(\tilde{Y}_{A};\tilde{Y}_{B},\tilde{Y}_{C},\tilde{Y}_{R}^{(1)},\tilde{Y }_{R}^{(2)}\right)\) in (26) is approximated as  
\[I\left(\tilde{Y}_{A};\tilde{Y}_{B},\tilde{Y}_{C},\tilde{Y}_{R}^{(1)},\tilde{Y }_{R}^{(2)}\right)=\log_{2}\frac{(p_{A}+\mu^{-1})\beta_{2}}{\beta_{3}}\approx \log_{2}\frac{\mu p_{A}}{2}. \tag{34}\]  
Similarly, \(I\left(\tilde{Y}_{A},\tilde{Y}_{R}^{(1)},\tilde{Y}_{R}^{(2)};\tilde{Y}_{B}, \tilde{Y}_{C}\right)\) in (29) is approximated as  
\[I\left(\tilde{Y}_{A},\tilde{Y}_{R}^{(1)},\tilde{Y}_{R}^{(2)}; \tilde{Y}_{B},\tilde{Y}_{C}\right) =\log_{2}\frac{\beta_{1}[p_{B}p_{C}+\mu^{-1}(p_{B}+p_{C})+\mu^{ -2}]}{\beta_{3}}\] \[\approx\log_{2}\frac{\mu^{2}p_{B}p_{C}}{8}. \tag{35}\]  
Therefore, based on the expression of \(C_{s}^{U}\) in (24), we can further infer that in the high SNR regime,  
\[C_{s}^{U}\approx\log_{2}\frac{\mu p_{A}}{2}. \tag{36}\]  
Consequently, combining (33) and (36), the difference between \(C_{s}^{U}\) and \(R_{s}\) is  
\[C_{s}^{U}-R_{s}\approx\log_{2}\frac{3}{2}. \tag{37}\]
## 4 Results
### Correlation Analysis  
The correlation analysis aims to give us more clarity about the relationships between collected data for different parameters and how they are related to one another. By understanding these relationships, we can gain insights into the dynamics of the environment being monitored.  
The correlations observed between temperature and dew point are positive (0.87) (Figure 8). This strong positive correlation is expected, as the dew point is directly influenced by temperature and humidity levels. When the temperature rises, the dew point also tends to increase, provided the moisture content in the air remains relatively stable. The correlation between PM2.5 and PM10 levels is notably high (0.79), indicating that these two measurements of particulate matter tend to move together. This is logical, as PM2.5 is a subset of PM10, and both are often influenced by similar sources, such as dust and other particulate-generating activities within the barn. TVOC and eCO2 also show a strong positive correlation (0.92), indicating that these two parameters are closely linked in the environment in the barn. This could be due to shared sources, such as animal respiration, waste management practices, and the use of certain chemicals used for milking devices and tanks' cleaning. Another significant correlation is observed between NH3 and H2S, with a correlation coefficient of 0.93, both of which are odorous substances that significantly impact air quality and can affect the taste of animal products. A strong positive correlation between these gases suggests they often increase together, likely due to shared sources like animal waste. Temperature is also correlated with NH3 and H2S, implying that higher temperatures could exacerbate the presence of these gases.  
Figure 7: **(a)** Ammonia (NH3) over time and **(b)** hydrogen sulfide (H2S) over time. Each chart includes a blue line representing the raw data points, along with a green trend line that captures the overall direction of the data.
# Research on the Hydrodynamic Performance of a Pentamaran in Calm Water and Regular Waves
## 4 Numerical Modelling  
### Computational Domain and Boundary Conditions  
Since the hull model is symmetrical with respect to the mid-longitudinal section, the numerical modeling can be taken in half, with the advantage of saving computing resources and time. For different simulations with calm water and regular waves, the ranges of the background domain were set to be consistent in the y and z directions, and the domain extended from \(-1.5<y/L<0\), \(-1.5<z/L<0.5\). The size of the domain was different for the two type cases in the x direction, in which \(-4<x/L<1\) for the calm water and \(-3<x/L<1\) for the regular waves. The reduced distance for the regular waves aimed to reduce the number of meshes and save computing resources.  
The boundary conditions were set as follows: the velocity inlet boundary conditions were applied on the front, top, and bottom of the background domain; the pressure outlet boundary condition was applied on the end of the background domain; two sides were set as the symmetry boundary condition; three surfaces of the overset domain were set as the overset mesh boundary condition; and the surfaces of the hull were set as the non-slip boundary condition. The computational domain and boundary conditions are shown in Figure 5.
# Device-to-Device (D2D) Multi-Criteria Learning Algorithm Using Secured Sensors
## 2 Related Work
The authors of [27] presented QL-MAC based on Q-learning, which iteratively tweaks the MAC parameters through a trial-and-error process and attains energy-efficient communication. It offers minimization problems without predetermining the system model, and also provides a self-adaptive protocol in case of topological or any external events. It readjusts the duty cycle of nodes and explicitly minimizes the energy consumption. The large-scale simulation experiments demonstrate its efficacy over other schemes. It was noticed that technologies of IoT and sensors are performing an extraordinary role in the development of smart communication. The sensors are widely used in different applications, including remote operations to observe the data and respond with a timely reaction [28; 29; 30]. However, they are bound in terms of resources and limit the online services for IoT networks. Moreover, transporting sensitive data from network devices towards the data centers is another important characteristic for any IoT-enabled system. It has also been seen that different solutions are discussed to improve the energy consumption and QoS parameters by using artificial intelligence and machine learning techniques for D2D communication; however, most of the reinforcement learning solutions lack the optimal consumption of resources, especially in the routing phase for mobile devices. In addition, they are not able to cope with the dynamic evaluation of routing links, and in such cases, sensors' data were frequently dropped. Moreover, it was also observed that a few solutions are still vulnerable to external attacks and not able to cope with data security under mobile nodes. Such solutions could not provide a robust mutual authentication system, and as a result, communication performance is non-collaborative and uncertain. Table 1 summarizes the discussion of the existing solutions.
# Sitting Posture Recognition Systems: Comprehensive Literature Review and Analysis
## 3 Sensing Technologies
### Pressure Sensor
#### 3.1.1 Pressure Sensor Types
The textile sensor array of 240 elements was embedded in a cushion as shown in Figure 3 (1: common electrode, 2: spacer, 3: sensing electrodes, and 4: electrodes switched to ground). Transducers that transform mechanical force into electrical impulses are called load cells (LCs). Usually, they are made of a metal frame on which strain gauges are fastened. The strain gauges stretch or compress as a result of the load cell's small deformation in response to force exerted. The strain gauges' electrical resistance is altered by this deformation, which modifies the voltage output in proportion to the force exerted. Load cells are versatile, reliable, wide range, and exhibit high accuracy, but they are expensive  
Figure 3: Textile pressure sensor with 240 elements [77]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Sensor Type** & **Material** & **Model** \\ \hline
[60,71] & FSR & Nanomaterial-sensitive film & DF9-40 \\
[72] & FSR & Thin film & IMS-C20A \\
[67] & FSR & - & - \\
[61,73] & FSR & Polymer & FSR 406 \\
[74] & FSR & - & - \\
[75] & FSR & polymer material & FSR01 CE \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Force-sensitive resistors are used for posture recognition. and require calibration regularly while being sensitive to environmental conditions. In the context of posture recognition, load cells are used to measure the distribution of body weight and pressure exerted on different parts of a surface, such as a seat plate. [PERSON] et al. [78] used load cells such as P0236-I42 on the seat plate to measure body weight distribution and categorize postures by employing machine learning with an accuracy ranging from 96.31% to 97.94%. Researchers in [79] placed 3 LCs over a cushion and classified eight distinct postures, attaining an overall classification accuracy of 98.50%.
# Analysis and Design of Protection Device for Anchor Cable Pull-Out in High-Stress Roadways
## 3 Tensile Test of Spring Protection Devices
### Tensile Test of Spring Protection Devices  
The anchor protective device had two specifications: the protective device with a spring wire diameter of 4 mm and 15 active turns and the protective device with a spring wire diameter of 5 mm and 12 active turns. The diameter of both springs was 22 mm. The relevant parameters of the protective devices were obtained through tensile test, providing a reference basis for further studying protective devices.  
Ten protective devices with a spring wire diameter of 4 mm and ten with a spring wire diameter of 5 mm were prepared for the test. The test process was as follows. The \"cross bar\" of a T-shaped steel was fixed with a spring hanger by using steel wires. The \"vertical bar\" was held by a gripper on the tensile machine. A hole was drilled onto the spring base for a high-strength long rod screw to pass through. This screw was fixed with bolts. The long rod of the screw was held by the gripper under the tensile machine for the tensile test. The test results were averaged, followed by curve fitting. The tensile loading rate was 50 mm/min. The test process is displayed in Figure 7, the test results of the protective device with a wire diameter of 4 mm are presented in Figure 8, and the test results of the protective device with a wire diameter of 5 mm are presented in Figure 9.  
As obtained from the test, 77.7 J of energy was absorbed when the protective device with a spring wire diameter of 4 mm was stretched, while 1092 J of energy was absorbed when it was stretched by 0.83 m. In addition, 1424 J of energy was absorbed in case of ultimate stretching by 1.03 m.  
For the protective device with a spring wire diameter of 5 mm, 77.7 J of energy was absorbed when it was stretched by 0.1 m, while 1308 J of energy was absorbed in case of ultimate stretching by 0.83 m.  
In summary, both protective devices can bear the impact kinetic energy of anchor wire breaking and ejection. The energy absorption efficiency of the protective device with a  
Figure 7: Test flowchart of the protective devices for anchors.  
spring wire diameter of 5 mm was superior to that of the protective device with a spring wire diameter of 4 mm. However, the ultimate energy absorption capacity of the former was smaller than that of the latter.
## 5 Experimental Results and Discussion
Figure 15 shows that, as a result of the use of SiC-MOSFETs, the system efficiency of the SiC-MOSFET-based PMSM direct drive system is higher than the Si-IGBT-based system in the both MTPA control and proposed system efficiency improvement control. As the SiC-MOSFET power device has lower switching losses than the Si-IGBT power device, the switching losses' ratio of system losses in the SiC-MOSFET-based PMSM is lower than that in the Si-IGBT-based PMSM system. Therefore, it can be seen that, compared with conventional the SiC-MOSFET-based PMSM system, the proposed control strategy can have better efficiency optimized performance in the conventional Si-IGBT-based PMSM direct drive system. The experimental results show that the proposed system efficiency improvement control strategy aims to decrease the system losses by optimizing both motor losses and inverter losses, and improves the system efficiency under each operational condition. The PMSM direct drive system with either  
Figure 14: System efficiency of the SiC-MOSFETs-based PMSM direct drive system by the MTPA control and the proposed system efficiency improvement control method. (**a**) System efficiency at rated speed; (**b**) system efficiency at rated torque. Figure 13: Efficiency of the SiC-MOSFETs-based PMSM drive system by the traditional MTPA control and proposed system efficiency improvement control. (**a**) Motor efficiency at rated speed; (**b**) inverter efficiency at rated torque. Si-IGBT or SiC-MOSFET can achieve lower energy consumption by using the proposed control method over the whole operation range of EVs.
## 3 Research Methods  
### Subjective Physiological Experiments  
#### 3.1.1 Subjects Data and Chamber Environments  
16 human subjects were invited for experiments and the resulting data volume is 1.44 million images. The experiments were conducted in a chamber with controllable indoor air temperature and relative humidity. The corresponding dry-bulb air temperature is 22.2 \(\pm\) 0.2 \({}^{\circ}\)C and the relative humidity is 36.9 \(\pm\) 2.5%. The resolution of vision sensor used for capturing video is \(1280\times 720\). The iButton, model DS192H with uncertainty \(\pm\)0.125 \({}^{\circ}\)C, was used for measuring skin temperature from the back of subject's hand. All the subjects are Asian females with an average age of 23.9 \(\pm\) 3.9 years, average weight of 52.2 \(\pm\) 6.5 kg, and body mass index (BMI) 19.9 \(\pm\) 2.2 kg/m\({}^{2}\).