esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### Fragility Analysis
#### 4.3.1 Effects of the Vibration Control System on the Seismic Vulnerability of the Cabinet  
Figure 14 shows a comparison of fragility curves for the electric cabinet when subjected to various ground motion groups. For each subfigure, the probability of the exceedance of the electrical cabinet installed with and without a TMD system is plotted. The mean \((\theta)\) and the standard deviation \((\beta)\) are determined from the Equations (8) and (9) and summarized in Table 3.  
From figure, it can be seen that the structural model without TMD would be more vulnerable than that model with the TMD system. The differences between the fragility curves are explained due to the differences in the acceleration responses of the structure. It is worth mentioning that due to the placement of the TMD at the top of the cabinet, the top response has reduced significantly.
# Preparation and Characterization of Gold Nanorods Coated with Gellan Gum and Lipoic Acid
###### Abstract  
Gold nanorods (AuNRs) can combine therapeutic hyperthermia with diagnostic features, representing a smart choice to address personalized cancer treatments. In this regard, a crucial quest is the selection of the right biocompatible coating agent able to stabilize them in the physiological environment, further endowing the possibility to load bioactive molecules and/or targeting moieties. Therefore, AuNRs optical properties can be successfully merged with advantageous materials features to obtain selective photothermal therapy (PTT) systems. Here, the natural materials lipoic acid (LA) and the polysaccharide gellan gum (GG) were chosen to prepare three types of stabilized gold nanorods, using LA (AuNRs/LA), a layered coating of LA and GG (AuNRs/LA,GG) or a newly synthesized covalent derivative of LA and GG (AuNRs/GG-LA). The samples displayed diverse stability and dispersibility. Hydrodynamic diameters and surface potential analyses confirmed the nanometric size (100-200 nm) and showed surface charges ranging from +19.5 to -25.6 mV. Particular attention was thus paid to analyze the differences between hyperthermia properties exhibited after near-infrared (NIR) laser irradiation. Furthermore, the cytocompatibility and photothermal effect were tested on HCT116 human colon cancer cell line. Collected data have finally allowed selecting AuNRs/LA,GG as the best candidate for possible use in PTT of cancer.  
gold nanorods; polysaccharide coating; gellan gum; lipoic acid; hyperthermia; photothermal therapy +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Photocatalytic Activity Evaluation
#### 3.5.1 Composition of the Rice Husk Extract before the Photocatalytic Reaction
Table 1 reports the presence of compounds containing aldehydes in the rice husk extract: at 6.91 min, furfural was detected in a small amount, and at 9.18 min, 5-methyl 2-furancarboxaldehyde was detected, with 0.43% area coverage, as also reported by [40]. **Ketones in the rice husk extract:** Table 1 reports the presence of ketone-containing compounds in the rice husk extract before the photocatalytic reaction. The peak at the retention time of 5.68 min corresponds to 1-hydroxy 2-butanone, that at 6.41 min to dihydro 2-methyl 3(2H)-furanone, that at 8.24 min peak to 2-methyl 2-cyclopenten-1-one, that at 8.31 min to1-(2-furanyl) ethanone, that at 10.18 min peak to 3-methyl 1,2-cyclopentendione, and that at 21.59 min to 3-carbethoxy-2-piperidone [40]. **Alkanes in the rice husk extract:** A peak near 3.53 min in Table 1 indicated the presence of cyclohexane, and a short peak at 4.90 min corresponds to 1,1-diethoxy ethane. Figure 7: Organic compounds in rice husk extract before the photocatalytic reaction. **Other compounds in smaller quantity:** Some other compounds were also detected by GC-MS in the rice husk extract but in minute quantity, as reported in Table 1. They included amines with a retention time of 3.73 min corresponding to 1,1-bis(2,2-dimethylpropoxy) N,N-dimethyl methanamine and of 7.54 min corresponding diethyl (3-decyn-1-yl) amine, with percentage areas of 0.20 and 0.21, respectively. At 6.54 min, a smaller peak with % area of 0.44 represents butyl ester acetic acid. An organic aromatic compound containing methyl and hydroxyl groups, named cresol, was also found in the rice husk at 12.50 min, with 0.45 of % area. Palmitate compounds also originated from the rice husk extract with, nearly 25 min of retention time. At 27.73 min and 27.83 min, the peaks with % area of 1.66% and 0.83% indicated the presence of glycolyl palmitate, whereas the peak appearing at 27.94 min, corresponded to glycerol 1-palmitate, with 1.14% area.
## 3 Model-Based System Engineering for Preliminary Design
### Integration of System Requirements within the Capella System Model  
In the \({}^{3}\)ColStar mission, the Arcadia/Capella methodology was employed to ensure comprehensive integration of the system needs, based on its architecture, into the model. Capella, an open-source MBSE software (version 6.1), facilitated the transition from a traditional document-based approach to a dynamic model analysis and traceability of functionalities throughout the mission.  
* System needs and and Integration: Requirements were established initially from mission concept, translated into Capella, and linked to various model elements, including operational scenarios, system functions, logical components, and physical designs. For example, the requirement for the solar weather payload to measure solar radiation with \(\pm 5\%\) accuracy was captured in Capella's requirement management module. This requirement was then linked to specific system functions and logical components responsible for implementing this functionality.
* Bidirectional Traceability: Bidirectional traceability was established to ensure that each requirement could be traced forward to its corresponding design elements and backward to its source. This was achieved by creating traceability links between the requirements and their associated system functions, logical components, and verification test cases. For instance, the requirement for data transmission every 30 min was linked to the design specifications of the IoT payload, ensuring that all aspects of the requirement were addressed. This was validated with the link budget model developed by the communications team.
* Connectivity Diagrams and Matrices: Capella provided diagrams and matrices to visualize the relationship between requirements and their corresponding design elements. These representations were used to verify that all components, functions and actors were fully addressed and to monitor how changes in the architecture impacted the design. For example, traceability matrices from the system's physical level showed, from multi-physics analysis, the alignment of the solar weather payload's accuracy requirement with its corresponding design elements and test cases.
* Validation and Verification: From the system architecture based on the Capella model, the validation and verification were established by using additional tools, such as Ansys STK, NASA GMAT, Matlab and Simulink, throughout the project. From the component to system level, each part was linked to test cases and validation criteria, enabling systematic verification of compliance with mission objectives. For example, test cases related to the solar radiation measurement accuracy were linked to the specific design elements responsible for this function, ensuring that the requirements were validated through testing.  
Each subsystem of the satellite was designed and integrated into the Capella model, ensuring complete traceability and alignment with mission objectives.
# Prototype of 5G Integrated with TSN for Edge-Controlled Mobile Robotics
## 2 Use Case Mobile Robotics and Requirements
### Requirements toward the Communication Infrastructure  
In addition to wireless communication, mobile robots need reliable and deterministic communication. In this use case, the robot has a LiDAR (Light Detection and Ranging) system for detecting the environment, a camera system for precise positioning of the tool, and a cloud-based PLC. These three different data streams have different requirements regarding reliability and real time.  
To define the requirements, the traffic types and characteristics given in [22] and 5G-ACIA are taken as reference, and the requirements are extended based on our industrial use case. Table 1 shows the traffic profiles for our use case. The Data Delivery Guarantee in the table indicates the aspect of the traffic's requirement which needs to be guaranteed to make sure the traffic does not experience any errors or interruptions. In a cloud-based PLC, Data Delivery Guarantee indicates the maximum bound of communication latency between the transmitter and receiver to make sure the connection is not dropped. However, in LiDAR, besides the latency bound, it indicates the minimum required throughput to transfer the data. For camera streaming, Data Delivery Guarantee shows the minimum required throughput to function properly. The camera traffic is a video stream with low criticality, giving information for the positioning of the tool. The LiDAR sensor sends continuous data triggering an event-based reaction in case of an identified dangerous situation. The cloud-based PLC sends cyclic data to the robot, controlling the movement and behavior of the system. Since mobile robots need to collaborate with other robots or machines, a shared time zone is required, and synchronization between the end devices is needed. Therefore, the traffic types and requirements can be defined as shown in Table 1. Generally, the cyclic PLC data need to be transmitted in a shorter time than the latency bound. The robot is not generally sensitive to jitter, or packet delay variations, as long as the latency bound is kept. However, if two mobile robots are collaborating, the jitter in the transmission can lead to some varying offsets in the movements of the two individual robots. Depending on the required synchronicity in movement of the robots, this may require an upper bound on the tolerable jitter. Alternatively, the control of the two robots can be made with a common time reference that is shared among the robots, e.g., by time synchronization over the network.
# A Novel Analytical Approach for Spacecraft Fly-Around Formation Design with a Low-Thrust Maneuver
## 4 Conclusions  
A guidance law for a servicer spacecraft autonomously flying around a target is presented in this study. The objective of the proposed guidance law is to enable two spacecraft in any initial relative states to form a closed elliptical or circular fly-around formation through a continuous low-thrust maneuver. A new series of formation configurations are enriched and developed. The control input of the guidance law consists of the feedforward term and the artificial term. The artificial gravitational coefficient and the vector of the artificial gravity center are the two key design parameters, which affect the spatial position, size, shape, and period of the fly-around formation. Analysis and numerical simulations are conducted to estimate the influence of the two parameters, and the feasibility of the guidance law is evaluated. The advantages of the proposed guidance law can be summarized as follows:  
1. The guidance law is intuitive and has good scalability. It is also suitable for circular reference orbit and elliptical reference orbit. It can also form a spatial fly-around formation and a specific planar formation. For proximity on-orbit operation missions, a stable fly-around formation can be quickly designed.
2. The fly-around trajectories are diverse. For certain initial relative states, there is a series of optional fly-around trajectories. According to different mission requirements, combined with the capacity of thruster and fuel constraints, reasonable design parameters can reach the desired goal.  
Conceptualization, X.W. and C.X.; methodology, X.W. and C.X.; software, X.W. and M.H.; validation, X.W. and M.H.; formal analysis, X.W.; investigation, X.W. and S.Z.; resources, X.W. and M.H.; data curation, X.W. and C.X.; writing--original draft preparation, X.W.; writing--review and editing, X.W. and S.Z.; visualization, X.W.; supervision, X.W. and C.X.; project administration, X.W. and C.X. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 12: Projected ellipse in the target’s orbital plane.  
Data Availability Statement: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
###### Abstract  
When working in humid environments, corrosion defects are easily produced in metallic plates. For defect detection in underwater plates, symmetric modes of Lamb waves are widely used because of their characteristics including long propagating distance and high sensitivity to defects. In this study, we extend our previous work by applying the laser laterally generated S\({}_{0}\) mode to detection and localization of defects represented by artificial notches in an aluminum plate immersed in water. Pure non-dispersive S\({}_{0}\) mode is generated in an underwater plate by lateral laser source irradiation and its _fd_ (frequency-thickness) range is selected by theoretical calculation. Using this lateral excitation, the S\({}_{0}\) mode is enhanced; meanwhile, the A\({}_{0}\) mode is effectively suppressed. The mode-converted A\({}_{0}\) mode from the incident S\({}_{0}\) mode is used to detect and localize the defect. The results reveal a significantly improved capability to detect defects in an underwater plate using the laser laterally generated S\({}_{0}\) mode, while that using A\({}_{0}\) is limited due to its high attenuation. Furthermore, owing to the long propagating distance and the non-dispersion characteristics of the S\({}_{0}\) generated by the lateral laser source, multiple defects can also be detected and localized according to the mode conversion at the defects.  
laser ultrasonics; underwater detection; S\({}_{0}\) mode; mode conversion +
Footnote †: journal: Applied Physics Letters  
**Keywords:** laser ultrasonics; underwater detection; S\({}_{0}\) mode; mode conversion
# A Millimeter-Wave Radar Tunnel Obstacle Detection Method Based on Invalid Target Filtering
## 2 Materials and Methods  
### Software, Hardware, and Information Acquisition  
The radar sensor used in this study is the ARS 408-21 XX from German company Continental A.D.C. GmbH, Memmingen, Germany. It operates at a frequency of 77 GHz and can detect multiple stationary and moving targets with relative velocities ranging from \(-\)400 to 200 km/h. It provides a long detection range of up to 250 m and can measure up to 256 moving targets with high reliability and small size, making it suitable for deployment on electric locomotives. The main performance parameters of the millimeter-wave radar are shown in Table 1. Its measurement performance shows that its operating temperature range is \(-\)40 \({}^{\circ}\)C to +85 \({}^{\circ}\)C, it conforms to IP 6k 9k (dust and high-pressure washing) IP 6k7 (underwater 10 cm), Ice water impact test, salt spray resistance, gas mixture EN60068-2-60, able to work in harsh environment. The sensor communicates with the host computer via CAN bus and outputs filtered target information. The hardware and software setup includes an Ubuntu 16.04 LTS operating system with ROS, an Intel Core i7-8700k CPU, the ARS 408-21 XX radar sensor, a vehicle, a CAN analyzer from Chinaray Technologies (Linux version), and a 12V-5A DC power supply from ADAPTER.
# Temperature Prediction Model in the Main Ventilation System of an Underground Mine
## 3 Methods  
### Data Collection  
Determination of the necessary data required to define the ventilation characteristics of the drifts in a mine was previously considered in a case study [27; 34]. Seven control points representing the main ventilation system were established, from the entry to the end of the intake, collecting data every 3 min between January 2016 and June 2019. The following variables were collected:  
- Time of the measurement (date and hour);  
- Control point (\(X\), \(Y\), and \(Z\) coordinates);  
- Actual distance between control points (m);  
- Dry temperature (\({}^{\circ}\)C);  
- Wet temperature (\({}^{\circ}\)C);Effective temperature (\({}^{\circ}\)C);  
Airflow (m\({}^{3}\)/s).  
Wet and dry temperatures are crucial to understanding the environmental conditions in an underground mine [35]; however, an analysis of the humidity evolution along the intake does not show appreciative changes. Therefore, the effective temperature, described by Spanish law (RGNBSM, ITC 04.7.02), was taken into account as a predictive variable, which includes wet and dry temperatures combined into a single value with a certain proportion, as shown in Equation (1).  
\[T_{ef}=0.9T_{w}+0.1T_{d} \tag{1}\]  
where \(T_{\mathcal{G}}\) is the effective temperature (\({}^{\circ}\)C), \(T_{w}\) is the wet temperature (\({}^{\circ}\)C), and \(T_{d}\) is the dry temperature (\({}^{\circ}\)C).
# Derivation and Verification of Laminated Composite T-Beam Theory
## 2 Derivation of the Formula for Laminated T beam
### Centroid Position in 2D Theoretical Analysis
#### 2.3.2 Centroid Position of Nonsymmetrical Laminated Composite T-Beams
\right]\left\{ \begin{array}{c} N_{x}\\ 0\\ M_{x}\\ M_{y}\\ M_{xy}\end{array} \right\} \tag{51}\]  
The moment of the narrow flange is calculated using Equation (26):  
\[M_{flange}=b(N_{x}Z_{1}+M_{x})=b\Bigg{(}\frac{Z_{1}^{2}}{A_{11,f}^{-1}}+\frac{1}{D_ {11,f}^{-1}}\Bigg{)}\kappa_{x} \tag{62}\]  
The moment of the beam is the sum of the moments of the flange and web, which is derived using Equations (61) and (62):  
\[M_{beam}=M_{web}+M_{flange}=\Bigg{[}\Bigg{(}\frac{h^{3}}{12A_{11,w}^{-1}}+\frac{ Z_{c}^{2}h}{A_{11,w}^{-1}}\Bigg{)}+b\Bigg{(}\frac{Z_{1}^{2}}{A_{11,f}^{-1}}+ \frac{1}{D_{11,f}^{-1}}\Bigg{)}\Bigg{]}\kappa_{x}=EI_{eff}\kappa_{x} \tag{63}\]  
From Equation (63) the effective stiffness of the beam is given as:  
\[EI_{eff}=\Bigg{(}\frac{h^{3}}{12A_{11,w}^{-1}}+\frac{Z_{c}^{2}h}{A_{11,w}^{-1}} \Bigg{)}+b\Bigg{(}\frac{Z_{1}^{2}}{A_{11,f}^{-1}}+\frac{1}{D_{11,f}^{-1}} \Bigg{)} \tag{64}\]  
The strain of the beam in the x-axis direction is expressed as shown in Equation (65)  
\[\varepsilon_{x}=z\kappa_{x} \tag{65}\]  
The strains of the flange in the x- and y-axis directions are given as:  
\[\varepsilon_{x}=\varepsilon_{x}^{0}+\xi x_{x}=(Z_{1}+\xi)\kappa_{x},\ \varepsilon_{y}= \varepsilon_{y}^{0}+\xi x_{y}=\Bigg{[}\frac{A_{21,f}^{-1}}{A_{11,f}^{-1}}Z_{1 }+\xi\frac{D_{21,f}^{-1}}{D_{11,f}^{-1}}\Bigg{]}\kappa_{x} \tag{66}\]  
The shear strain of the flange in the xy plane is:  
\[\gamma_{xy}=(Z_{1}\frac{A_{31,f}^{-1}}{A_{11,f}^{-1}}+\xi\frac{D_{31,f}^{-1}} {D_{11,f}^{-1}})\kappa_{x} \tag{67}\]  
Therefore, the total strain of the narrow flange is given as:  
\[\left\{ \begin{array}{c}\varepsilon_{x}\\ \varepsilon_{y}\\ \gamma_{xy}\end{array} \right\}=\left\{ \begin{array}{c}Z_{1}+\xi\\ Z_{1}\frac{A_{21,f}^{-1}}{A_{11,f}^{-1}}+\xi\frac{D_{21,f}^{-1}}{D_{11,f}^{-1 }}\\ Z_{1}\frac{A_{31,f}^{-1}}{A_{11,f}^{-1}}+\xi\frac{D_{31,f}^{-1}}{D_{11,f}^{-1}} \end{array} \right\}\kappa_{x} \tag{68}\]  
The stress on the beam is derived from the stress-strain relation, as shown in Equation (69):  
\[\left\{ \begin{array}{c}\sigma_{x}\\ \sigma_{y}\\ \tau_{xy}\end{array} \right\}=\left[ \begin{array}{ccc}\overline{Q}_{11}& \overline{Q}_{12}&\overline{Q}_{16}\\ \overline{Q}_{21}&\overline{Q}_{22}&\overline{Q}_{26}\\ \overline{Q}_{61}&\overline{Q}_{62}&\overline{Q}_{66}\end{array} \right] \left\{ \begin{array}{c}\varepsilon_{x}\\ \varepsilon_{y}\\ \tau_{xy}\end{array} \right\} \tag{69}\]
## 1 Introduction
The researchers embedded various machine learning models to detect falls, reporting 97.1% accuracy and an F1 score of 0.970 using a support vector machine model. The anomaly data, generally difficult to collect, are required in order to train classification models for the anomaly detection. [PERSON] et al. [20] tested the performance of a fall-detection model by adjusting the proportion of fall data during training and showed no significant difference in model performance even though only 77% of fall data was used. This suggests that sufficient performance could be achieved even with a small amount of anomaly data. In this study, falls during e-scooter riding were detected using an IMU sensor before the impact occurred at the ground. Three different approaches were taken for the pre-impact fall detection algorithm: threshold-based, supervised, and unsupervised models. The threshold-based algorithm was proposed using the magnitude of the acceleration, the angular velocity, and the roll angles from the 6-axis IMU data. A supervised learning approach was employed using deep learning models to classify normal and fall scenarios. Finally, an unsupervised learning model was developed using autoencoder models. In addition, knowledge distillation techniques were applied to the unsupervised model in order to reduce the model size, while performance was maintained.
## 2 Study Area  
Poyang Lake, the largest freshwater lake in China, was selected as the study area. Poyang Lake is located in the northern Jiangxi Province (29\({}^{\circ}\)05\({}^{\prime}\)-29\({}^{\circ}\)17\({}^{\prime}\)N and 115\({}^{\circ}\)54\({}^{\prime}\)-116\({}^{\circ}\)08\({}^{\prime}\)E, southeastern China) and outflows into the Yangtze River at Hukou. The average water surface area of Poyang Lake is more than 2500 km\({}^{2}\) during the wet season [6]. The lake is the primary component of the Poyang Lake wetland and has been included in the first set of the Ramsar Convention List of Wetlands of International Importance [7]. The Poyang Lake region experiences a subtropical monsoon climate that is characterized by a rainy season (April to September) and a dry season (October to March) [6]. The annual average temperature is 25 \({}^{\circ}\)C, and the annual average accumulative precipitation is approximately 1600 mm [31]. The main water sources into the Poyang Lake are local precipitation and rivers (including the Xiushui, Ganjiang, Fuhe, Xinjiang, and Raoe rivers, Figure 1) [6].  
Numerous small water bodies in the periphery have been disconnected from the large lake system because more than 500 levees have been built in Poyang Lake to protect approximately 5082 km\({}^{2}\) of farmland and settlements [32; 33]. In addition, the boundary of the water surface of Poyang Lake is unstable and needs to be defined. The outline of the maximum water surface areas (on 17 July 2016) was defined as being approximately 4105 km\({}^{2}\) based on a previous study (Figure 1) [6].  
Another minor study area is Taihu Lake, which is located in Jiangsu Province, China (30\({}^{\circ}\)56\({}^{\prime}\)-31\({}^{\circ}\)33\({}^{\prime}\)N and 119\({}^{\circ}\)52\({}^{\prime}\)-120\({}^{\circ}\)36\({}^{\prime}\)E) and is one of China's five major freshwater lakes. This area was used to examine the results of the SWI-fitted equation and to examine the classification accuracy of S1A.