esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Formation of Heterogeneous Groups
### Clustering Techniques for the Formation of Homogeneous Clusters
#### 3.1.3 Agglomerative Clustering  
The agglomerative clustering is a method of hierarchical clustering, and it is used to group objects in clusters based on their similarity.  
Algorithms in this category generate a cluster tree (or dendrogram) by using heuristic splitting or merging techniques [37]. A cluster tree is defined as \"a tree showing a sequence of clustering with each clustering being a partition of the data set\" [38].  
Agglomerative clustering is also known as AGNES (Agglomerative Nesting) is a bottom-up approach. This algorithm starts by treating each object as a singleton cluster. Then, pairs of clusters are successively merged until all clusters are merged into one big cluster containing all objects. The result is a tree-based representation of the objects.  
The steps we need to perform to achieve clustering using the agglomerative clustering algorithm are:  
1. The first thing we need to do is to consider each data point as a cluster.
2. During this phase we take the two closest clusters and try to merge them into a cluster.
3. We repeat step 2 until all clusters are merged together.
4. End.
## 3 Results and Discussions
### Identification of the Key Arroma Characteristics of RAPT during Storage  
Floral, fruity, and green aromas are considered favorable aroma characteristics in tea. Therefore, we analyzed the floral and fruity aromas of volatile compounds in RAPT. In Figure 4A, it is shown that most of the volatile compounds in RAPT have pleasant floral and fruity aromas during storage. Previous results have also indicated that the floral and fruity aromas increased over time during the first four years of storage. RAPT is unique compared with other teas because it undergoes autoclaving and compressing treatments. These treatments result in an increase in the content of certain aroma compounds such as phytol, linalool, and geraniol, which have a significant contribution to the floral aroma of tea [31]. Consequently, RAPT has a distinctive floral and fruity aroma, which may be dramatically changed during storage. As shown in Figure 4B, the roasted aroma, particularly that of 1-ethyl-1H-pyrrole, gradually diminished over the storage period. After ten years of storage, the green aroma decreased, while the woody aroma increased. However, it is important to note that the floral and fruity aromas remained dominant, indicating their overall persistence in the RAPT.  
In addition, the intensity of the floral and fruity were gradually enhanced during storage. Hence, eight aroma compounds were screened with OAV \(\geq\) 100, namely linalool (OAV: 2691.01-8040.2), \(\alpha\)-terpineol (OAV: 250.8-491.5), geraniol (OAV: 99.5-258.3), trans-\(\beta\)-ionone (OAV: 40730.3-108493), \(\alpha\)-ionone (OAV: 274.1-562.5), (E,E)-2-4-heptadienal (OAV: 3248.6-5892), 1-octanol (OAV: 95.2-620.8), and octanal (OAV: 1320.7-4131.4), as illustrated in Table S2. Interestingly, all of them contribute to the aroma of floral and fruity. Notably, (E,E)-2,4-heptadienal is one of the dominant volatile compounds in high-grade Jingshan green tea, and it is produced through the degradation of linelonic acid, providing flowery aroma [32]. In oolong tea (_Camellia sinensis_), geraniol and linalool are the main contributors to the floral, rose-like, and sweet aroma characteristics. In addition, [PERSON] et al. [33] found that trans-\(\beta\)-ionone contributed to the aroma characteristics of Rougui tea. Therefore, these eight volatile compounds are considered to be the key contributors to the floral and fruity characteristics during RAPT storage.  
Figure 3: Based on OAV > 1 analysis of 43 volatile compounds and during RAPT storage.
# Delamination Detection in Bimetallic Composite Using Laser Ultrasonic Bulk Waves
## 3 Experimental Setup and Specimen  
### Experimental Setup  
Figure 2 shows the schematic diagram of the laser ultrasonic testing system. A Q switched neodymium-doped yttrium aluminium garnet (Nd: YAG) pulsed laser (Ultra-50) manufactured by Quantel with pulse duration of 8 ns, laser spot diameter of 1 mm, wavelength of 1064 nm, and energy range of 0-50 mJ was used for generating ultrasonic waves. The ultrasound was detected by a two-wave mixing (TWM) laser interferometer system. The wavelength of the continuous laser in the interferometer system was 1550 nm and the maximum power was 2 W. The pulsed laser emitted a laser pulse to the surface of the aluminum layer to excite ultrasonic waves, and simultaneously sent a synchronous signal to the data acquisition card (NI PCI-5114) to trigger it to start collecting data. The interferometer converted the vibration displacement of the ultrasonic wave into a voltage signal and then outputted it to the data acquisition card. The data were filtered, saved, and displayed on the computer. The Butterworth filter was used for low-pass filtering to filter out high-frequency noise above 10 MHz. The computer sent instructions to the motion controller to control the movement of the two-dimensional platform and complete the detection of the designated area of the sample. During the experiment, the detection laser should be kept perpendicular to the sample surface to ensure that enough reflected light is received by the detection probe. The excitation probe was fixed on the precision displacement platform to set the relative position of the excitation point and the receiving point.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Physical Properties** & **Cu** & **AI** \\ \hline Thermal conductivity (W m\({}^{-1}\) K\({}^{-1}\)) & 386.4 & 209 \\ Density (g cm\({}^{-3}\)) & 8.96 & 2.71 \\ Poisson’s ratio & 0.326 & 0.33 \\ Thermal expansion coefficient (10\({}^{-6}\) K\({}^{-1}\)) & 17.2 & 23.6 \\ Elastic Young’s modulus (GPa) & 119 & 68 \\ Specific heat (l kg\({}^{-1}\) K\({}^{-1}\)) & 394 & 880 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The thermo-physical parameters of Cu and Al used in the finite element (FE) models.  
Figure 1: Laser ultrasound in copper-aluminum composites: (**a**) a model without defects; (**b**) a model with delamination.
# Influence of Pre-Radiation and Photo-Bleaching on the Yb-Doped Fiber Laser Radiated with Gamma-ray
## 5 Conclusions  
To improve the radiation resistance of high-power Yb-doped fiber lasers, the influence of pre-radiation and photo-bleaching on the gamma-radiated laser's performance was researched in this work. The pre-radiated fiber laser and non-pre-radiated fiber laser were fabricated with the pre-radiated Yb-doped fiber and pristine Yb-doped fiber, respectively. Except for the active fiber, both fiber lasers had the same configuration. The laser performance of non-pre-radiated and pre-radiated fiber lasers after gamma radiation was comparatively investigated. When the gamma radiation dose is within 10 krad(Si) with the radiation dose rate of less than 0.4 rad(Si)/s, pre-radiation treatments would improve the radiation resistance against gamma-ray to a certain extent, showing less output power reduction. However, the mode instability threshold would be decreased, due to the cumulated radiation-induced attenuation caused by pre-radiation and radiation. Therefore, the radiation induced MI effect in high-power fiber lasers needs to be comprehensively considered before the pre-radiation treatment is implemented.  
Considering the doping element of this commercial Yb-doped fiber, a laser diode at 532 nm was applied as the photo-bleaching laser source. In this research, the output power of the fiber laser partly recovered by 26 W, and the slope efficiency also recovered from 72.1% to 74.8%. The MI threshold was also increased. According to the research on  
Figure 10: Quantitative research of photo-bleaching effect, (**a**) absorption intensity at 640 nm versus photo-bleaching energy, (**b**) bleaching efficiency versus the number of bleaching treatments.  
bleaching treatment of the radiated Yb-doped fiber, approximately 45.2% radiated-induced attenuation was bleached by the 532 nm laser, reaching the bleaching saturation state. This research is of significance in the design and fabrication of high-power fiber lasers against gamma radiation.  
Investigation, M.Y. and S.L.; Resources, Y.C.; Writing--original draft, S.S.; Writing--review & editing, Y.Z.; Project administration, J.W.; Funding acquisition, X.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (No. U20B2058).  
Not applicable.  
The data presented in this study are available from the corresponding author, upon reasonable request. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.
## 3 Platform Design Criteria
### Server Design Criteria  
The main purpose of the application server is to receive field data and store them reliably for later access upon request. It bridges the low-power communication segments (that operate under strict latency-energy trade-offs) with the fast and ubiquitous data access needed by human interfaces or IoT applications. As shown in Figure 1, the server provides interfaces for field nodes, operators and supervisors of the field, alert channels and for external systems.  
For a high QoS, the server should implement high-availability techniques, e.g., a secondary server should be kept in sync and automatically take over the service if the main server fails.  
The protocol between the nodes and the server should be simple to reduce the resource requirements for the nodes and, as much as possible, stateless, because the long-range connections at the fringe of mobile data coverage can be often unstable and the overhead for resuming an interrupted transfer should be minimized. For the same reason, the protocol should provide acknowledgments for data reception, and these should be sent only when the data are successfully replicated on the secondary server to avoid field data loss due to single points of failure.  
Gateways that need to enter the fail-safe operation mode may use a communication protocol with the server that is optimized for the operations executed in this mode (e.g., software update). The server should implement this protocol and the associated operations, as well as promptly notify the operators when a gateway connects using this channel, to take corrective actions.
# End-to-End Delay Model for Train Messaging over Public Land Mobile Networks
###### Abstract  
Modern train control systems rely on a dedicated radio network for train to ground communications. A number of possible alternatives have been analysed to adopt the European Rail Traffic Management System/European Train Control System (ERTMS/ETCS) control system on local/regional lines to improve transport capacity. Among them, a communication system based on public networks (cellular&satellite) provides an interesting, effective and alternative solution to proprietary and expensive radio networks. To analyse performance of this solution, it is necessary to model the end-to-end delay and message loss to fully characterize the message transfer process from train to ground and vice versa. Starting from the results of a railway test campaign over a 300 km railway line for a cumulative 12,000 traveled km in 21 days, in this paper, we derive a statistical model for the end-to-end delay required for delivering messages. In particular, we propose a two states model allowing for reproducing the main behavioral characteristics of the end-to-end delay as observed experimentally. Model formulation has been derived after deep analysis of the recorded experimental data. When it is applied to model a realistic scenario, it allows for explicitly accounting for radio coverage characteristics, the received power level, the handover points along the line and for the serving radio technology. As an example, the proposed model is used to generate the end-to-end delay profile in a realistic scenario.  
PLMN; end-to-end delay; channel modeling; train communications +
Footnote †: journal: Information
# Smart Irrigation Systems from Cyber-Physical Perspective: State of Art and Future Directions
## 3 Problem Space
### IoT Layer Dimension  
The IoT layers dimension provides a technological perspective, focusing on the vertical layers that constitute the framework of typical IoT systems. These layers support various smart-world applications [25; 26; 27; 28; 29]. (i) _Perception Layer:_ Also known as the physical layer, it includes all hardware components like sensors, nodes, actuators, controllers, and other devices responsible for data collection. Our review will focus on key devices such as sensors and microcontrollers, comparing their implementations in 34 complete system proposals. (ii) _Network Layer:_ Often referred to as the communication layer, it enables connectivity among physical devices and between devices and applications through both  
Figure 1: Three-dimensional (3D) problem space overview.  
wired and wireless means. Most proposals favor wireless technologies for their ease of setup, with prevalent technologies including Low-Power Wide Area Networks (LPWANs), Bluetooth, and WiFi. We will evaluate several pivotal edge technologies in this layer for their effectiveness. (iii) _Application Layer:_ This layer includes user interfaces and backend data management applications. Our analysis will concentrate on studies integrating AI, aiming to assess their performance. Although IoT platforms are well documented in the literature, we will discuss the most prominent platforms.  
Through the IoT Layers Dimension, our objective is to highlight the technologies, devices, and methodologies utilized in designing and implementing IoT-driven irrigation systems. This approach offers a thorough insight into how these interconnected layers contribute to system effectiveness. Figure 3 illustrates the architecture of a typical IoT system.
## 3 Numerical Study
### Numerical Model  
The numerical model consists of two parts: the soil and the GPF, as indicated by the laboratory experiment in Section 2.1; the soil material is fine sand, while the GPF is constructed from organic glass. To facilitate loading, a steel footing is placed on top of the pile foundation. The parameters of the model are shown in Table 2. The cross-section of the GPF is a regular hexagon with a thickness of 4 cm and a side length of 20 cm. The pile length is 90 cm, and it is embedded to a depth of 80 cm. The total depth of the soil is 150 cm.  
The soil is considered a linear elastic material and is characterized by the Mohr-Coulomb elastoplastic constitutive model. To simplify the calculation, the soil layer is considered a single layer of soil. The organic glass is treated as an isotropic material, and its behavior is simulated using the Isotropic Linear Elastic constitutive model. The steel components are considered isotropic materials, and to simulate potential rigid body rotations that may arise in pile foundations, a Bilinear Elastic-Plastic constitutive model has been utilized [34]. The numerical model is divided into grids starting from the pile core and extending outward. The grid density gradually decreases along the model boundaries to ensure a continuous state of the overall grid and prevent issues with stress transmission. In the vertical direction, a stretching method is employed from the bottom surface upward to ensure the continuity of the grid throughout the model, avoiding stress discontinuities [35]. Based on the experimental parameters and dimensions, a numerical model of the GPF is established as shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Material** & **Modulus of Elasticity** & **Cohesion** & **Internal Friction Angle** & **Density** & **Poisson Ratio** \\  & **E/MPa** & **c/kPa** & **\(\varphi\)/(\({}^{\circ}\))** & **\(\rho\)/kg.m\({}^{-3}\)** & **V** \\ \hline GPF & 2000 & - & - & 2500 & 0.18 \\ Soil & 60 & 10 & 22 & 1860 & 0.54 \\ Pile cap & 210,000 & - & - & 7850 & 0.30 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters for foundation, soil, and pile cap.  
During the indoor experiment, the GPF and the soil were placed in a test pit with dimensions of 200 cm \(\times\) 200 cm \(\times\) 160 cm, and the GPF was positioned at the center of the soil. In the experimental study, the press imposed by the walls of the test pit on the soil was considered a rigid constraint. In the numerical study, the model boundary condition was set so that six degrees of freedom of the bottom and four sides of the soil model were fixed. In the experiment, a unidirectional, linear loading was applied using displacement control. In the numerical simulation, the loading point was located at the top of the pile cap. The static pushover analysis with 100 steps was performed to reach a total displacement of 0.06 m.
# SatScope: A Data-Driven Simulator for Low-Earth-Orbit Satellite Internet
## 3 SatScope Model Framework
### Space Segment Modeling
#### 3.1.2 Dividing Orbits  
It is important to note that the satellites in the fifth shell, as detailed in Table 1, exhibit highly dynamic behavior. When applying the DBSCAN algorithm for orbital plane division, only a single cluster can be identified due to this high dynamism. To address the orbital plane segmentation of such highly dynamic shells, SatScope employs the RAAN and semi-major axis parameters.  
HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise), an extension of DBSCAN, builds upon a hierarchical structure to identify density variations within orbital planes, making it more effective for handling communication relay satellites in these planes. Unlike DBSCAN, HDBSCAN does not require pre-specifying the number of satellites within an orbital plane; instead, it automatically determines the number and structure of orbital planes by analyzing the density distribution of real-time satellite data. This capability allows HDBSCAN to manage uneven satellite distributions  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Shell Layer**} & **Average** & **Altitude** & **Average** & **Number of** & **Number of** \\  & **Altitude** & **Standard** & **Inclination** & **Satellites** & **Orbital** \\ \hline
**1** & 364.72 & 7.48 & 53.16 & 232 & 20 \\
**2** & 447.88 & 4.78 & 43.00 & 483 & 12 \\
**3** & 478.44 & 2.77 & 53.16 & 330 & 15 \\
**4** & 491.49 & 4.31 & 43.00 & 163 & 10 \\
**5** & 546.81 & 6.83 & 53.21 & 1519 & 74 \\
6 & 563.02 & 4.31 & 43.00 & 1345 & 27 \\
**7** & 575.24 & 8.68 & 97.66 & 232 & 5 \\
**8** & 582.55 & 6.80 & 70.00 & 391 & 21 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Numerical values related to shell clustering.  
Figure 4: The probability cumulative distribution of real\(-\)time satellites. Figure 4 depicts the clustering of satellites into different shells based on real\(-\)time altitude data using the DBSCAN algorithm, with distinct colors representing various shells. The parameters of DBSCAN are set as follows: \(\epsilon\) indicates that the neighborhood radius is 10; the minimum number of core points is 100; the number of noise points is 112; and the horizontal axis represents the real-time altitude of the satellites.  
and better adapt to dynamic changes in satellite constellations, thus enabling flexible and accurate real-time satellite data analysis.
## 2 Methodology
### Identification of SOTIF-Related Hazardous Factors with the Extended STPA  
The research of the SOTIF usually uses unintended behaviors of the intended function and reasonably foreseeable misuse as the direct causes of the hazard [5; 26; 27]. The unintended behaviors are caused by scenarios containing triggering events. The triggering events happen when the performance limitation affects the proper situational awareness. The research above is based on the assistant driving of road vehicles, in which the E/E system directly controls the train, and the driver plays the role of a backup. For the SOTIF of the IRDAS, since it is the human but not E/E system that controls the train, the hazard is directly caused by unintended human behaviors. Then, the unintended human behavior is caused by unintended behaviors of the IRDAS or reasonably foreseeable misuses, which is similar to the past research.  
STPA is a well-recommended method for SOTIF-related hazard analysis, and some works provide examples. ISO 21448 [5] provides an example that uses STPA in the hazard analysis of the reasonably foreseeable misuse. In the analysis of NHTSA [26], STPA is used in the analysis of both performance limitations and misuses. Their research shows that UCA is similar to unintended behavior, and causal scenario is similar to trigger event. The works above illustrate that STPA can suit the causation idea of the SOTIF. However, the way they identify causal scenarios about performance limitations is through the exhaustion of bidirectional comparison of component performance limitations and environmental elements. This process is not based on the complex interaction of their control structure. As a result, they only analyze the situation awareness components, including sensors and algorithms, in an isolated way, and neglect the integrated system. STPA uses the control structure to model and analyze the complex interactions and provides systematic causal scenarios. Thus, the way to construct the control structure and the way to deduct causal scenarios for the SOTIF need further customization.