esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results
### Optimized Recommendation Module Prediction Accuracy
#### 6.1.2 Topic Class Prediction Accuracy for a Varying Number of Topics  
Here, we experimented with the different number of topics for LDA training, for topic class prediction of text data. This way, we found the optimal number of topics for better predictions as well. We varied the number of topics between 5 and 20.  
As shown in Figure 10, the prediction accuracies change with a different number of topics. For all the algorithms used, we can observe that maximum accuracy is achieved when the number of topics is between 10 and 15. If the number of topics is 12, all the algorithms achieve their highest prediction accuracies.  
Figure 10: Prediction accuracy for a varying number of topics.  
Figure 9: Topic class prediction accuracy for different algorithms.
## 4 Surrogate Model Establishment  
### Experimental Space Design for Modeling  
Effective experimental space design is critical for establishing surrogate models that accurately capture system characteristics, including variable coupling effects and input-output mapping relationships. The primary objective is to strategically distribute limited sampling points to achieve maximum spatial coverage while maintaining balanced projection density across all dimensions.  
This study employs LHS optimized by the Morris-Mitchell criterion to generate 200 training samples in a three-dimensional design space [34]. Unlike random sampling, LHS ensures uniform marginal distributions through stratified division, where each dimension is partitioned into equal non-overlapping intervals with exactly one sample per interval. While basic LHS guarantees projection uniformity, its spatial coverage may be suboptimal due to random permutation along each axis, potentially resulting in clustered or unevenly distributed samples.  
To address this limitation, we applied the Morris-Mitchell space-filling criterion to optimize the spatial distribution of sample points [35]. For a given design matrix \(X\in\mathbb{R}^{n\times d}\) containing \(n\) samples in \(d\) dimensions, the optimality metric \(\phi_{p}\) is defined as  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Factor**} & \multicolumn{2}{c}{\(T_{avg}\)} & \multicolumn{2}{c}{\(\eta\)} & \multicolumn{2}{c}{\(\Delta T\)} \\ \cline{2-7}  & **SSX** & **Proportion** & **SSX** & **Proportion** & **SSX** & **Proportion** \\ \hline \(\delta_{g}\) & 0.0072 & 0.0022 & 0.0009 & 0.0003 & 0.0261 & 0.0111 \\ \(b_{r}\) & 0.0237 & 0.0071 & 0.0112 & 0.0036 & 0.3128 & 0.1327 \\ \(b_{m}\) & 0.6479 & 0.1950 & 0.6968 & 0.2298 & 0.3169 & 0.1344 \\ \(l_{s}\) & 0.0018 & 0.0005 &\[\phi_{p}=\left(\sum_{i=1}^{m}l_{i}\cdot d_{i}^{-p}\right)^{1/p} \tag{3}\]  
where \(d_{i}\) represents distinct pairwise Euclidean distances between samples, \(J_{i}\) denotes the number of point pairs separated by distance \(d_{i}\), \(m\) is the total number of different distance values, and we set \(p=10\) to control the metric's sensitivity to small distances. Minimizing \(\phi_{p}\) promotes uniform spatial distribution by penalizing clustered point arrangements.  
The optimized sampling configuration, visualized in Figure 9, demonstrates superior space-filling characteristics. The entire sample set achieves a global minimum inter-sample distance of 0.0985 (in normalized space) while maintaining uniform marginal distributions. Subsequently, finite element simulations conducted in ANSYS Maxwell 2024R1 generated paired input-output datasets to train and validate the surrogate model.  
This methodology ensures that the surrogate model's training set adequately captures the design space topology, thereby facilitating accurate modeling of complex electromagnetic relationships between input parameters and performance objectives.
# Artificial Intelligence-Based Discontinuous Reception for Energy Saving in 5G Networks
## 2 Related Work
### Non-Compatibility of LTE-DRX for 5G Networks  
The LTE-DRX mechanism in its existing form is not suitable for 5G networks. The main reason for non-suitability is the directional communications in 5G networks, which require beamforming operation prior to data transmission [4]. Whereas, LTE-DRX mechanism does not include beam searching operation [19; 20]. Figure 3 shows the concept of directional communication with multiple beams in 5G network. The gNB transmits \(K\) number of beams and a UE has \(L\) number of beams. A UE needs to search and align the best beam pair form \(K\times L\) beam pairs, prior to the start of communications [4]. The process of beam searching and beam alignment in 5G is not included in LTE-DRX. Moreover, the process of beam searching in 5G network causes a UE to remain in the active state more than that of the LTE-DRX, which also increases the power consumption of UE [21]. Furthermore, the beamforming process in 5G requires additional time for beam searching and aligning best beam pairs [22]. The additional beam searching and beam alignment time cause more delay than the delay in LTE-DRX. Hence, directional communications in 5G is one of the reasons, which makes LTE-DRX mechanism in its existing form, not a suitable solution for power saving in 5G networks [21; 22].  
The second main reason for non-compatibility of existing LTE-DRX mechanism with 5G networks is the fixed length of sleep cycles in LTE-DRX [11]. LTE-DRX uses fixed length short and long sleep cycles to economize the power consumption of UE. Whereas, 5G network is expected to deal with different types of services, simultaneously [4]. These services may have different size of packet lengths, variable packet arrival time and variable transmission time interval (TTI) [23]. The fixed length of sleep cycles in LTE-DRX mechanism may not be suitable for 5G services as these cycles may under-utilize or over-utilize TTI. Moreover, fixed length sleep cycles in 5G may be responsible for least power savings in 5G enabled devices [23]. However, DRX design can still be used for 5G [9; 24; 25].  
Figure 2: LTE-DRX timing diagram.
## 2 Air Pressure Compensation Based on IE-FF Coupling Model  
### IE-FF Coupling Modeling  
_Air pressure numerical analysis of elevator car 3D external flow field_. During the operation of high-speed elevators, the air pressure change outside the elevator car is a fluid motion problem. Therefore, Navier-Stokes (N-S) equations are introduced to numerically calculate the air pressure outside the car. N-S equations mainly include the continuous equation, momentum equation and energy equation. The basic expressions are as follows [29; 30]:  
\[\frac{\partial\rho}{\partial t}+\frac{\partial\left(\rho u_{j}\right)}{ \partial x_{j}}=0, \tag{1}\]  
\[\frac{\partial\left(\rho u_{j}\right)}{\partial t}+\frac{\partial\left(\rho u _{i}u_{j}\right)}{\partial x_{j}}=-\frac{\partial P}{\partial x_{i}}+\frac{ \partial\left(u_{i}\tau_{ij}\right)}{\partial x_{j}}, \tag{2}\]  
\[\frac{\partial\left(\rho e_{0}\right)}{\partial t}+\frac{\partial\left(\rho u _{j}h\right)}{\partial x_{j}}=div(\lambda\ grad(T))+\frac{\partial\left(u_{i} \tau_{ij}\right)}{\partial x_{j}}, \tag{3}\]where \(\rho\) is the fluid density; \(x_{i},\ x_{j}\ (i,j=1,2,3)\) denotes the scalar components of spatial-coordinates vector; \(u\) is the fluid velocity; \(P\) is the static pressure; \(T\) is the static temperature; \(e_{0}\) denotes the total energy; \(\lambda\) is the thermal conductivity; \(\tau_{ij}=\mu\left(\frac{\partial u_{i}}{\partial x_{j}}+\frac{\partial u_{j}}{ \partial x_{i}}\right)-\frac{2}{3}\mu\frac{\partial u_{i}}{\partial x_{k}} \delta_{ij}\) denotes the scalar components of Reynolds-stress tensor, and \(\mu\) is the dynamic viscosity; and \(h=e_{0}+\frac{p}{\rho}\) denotes the enthalpy. Considering that the real air flow in the hoistway during the operation of the high-speed elevator is an unstable three-dimensional turbulent flow, the air flow outside the car is regarded as an incompressible viscous fluid with no heat transfer. Under the assumption that the car is completely airtight, the turbulence model is used to represent the air flow outside the car. The Reynolds average method is used to simulate turbulence, and a standard two-equation \(k\)-\(\epsilon\) model is established for the simulation solution. Based on the basic principles of fluid mechanics, the continuity equation of the flow field and the Reynolds-averaged Navier-Stokes (RANS) equations are as follows [29;30]:  
\[\frac{\partial u_{i}}{\partial x_{j}}=0, \tag{4}\]  
\[\frac{\partial\left(\rho u_{i}u_{j}\right)}{\partial x_{j}}=-\frac{\partial P }{\partial x_{j}}+\frac{\partial}{\partial x_{j}}\left[\mu\left(\frac{ \partial u_{i}}{\partial x_{j}}+\frac{\partial u_{j}}{\partial x_{i}}\right) \right]+\frac{\partial}{\partial x_{j}}\left(-\rho\overline{u_{i}^{\prime}u_{ j}^{\prime}}\right), \tag{5}\]  
where \(u_{i}\) and \(u_{j}\) are scalar components of instantaneous fluid velocity \(u_{i}\); \(-\rho\overline{u_{i}^{\prime}u_{j}^{\prime}}\) represents the Reynolds stress.
# Significant Wave Height Estimation from Space-Borne Cyclone-GNSS Reflectometry
## 3 Results and Evaluation
### Accuracy Assessment  
#### 3.2.1 Comparing with Satellite Altimeter SWH Observation  
Comparing the SWH estimate with the AVISO SWH observation, as shown in Figure 10, since the spatial resolution of the AVISO SWH data is 1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\), and the spatial resolution of the SWH estimate is 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\), the SWH estimation value needs to be interpolated. It can be seen from Figure 10 that there is a highly similar distribution between the CYGNSS SWH estimate and the AVISO SWH observation in April 2017.  
Figure 11 shows the difference distribution histogram of the CYGNSS SWH estimate relative to the AVISO SWH. It can be seen that the difference is concentrated between \(-\)0.4 m and 0.4 m. Combined with the data in Table 2, we can see that the absolute value of the bias between estimates  
Figure 10: Comparison of CYGNSS SWH estimates (left) and AVISO satellite altimeter SWH values (right).  
and AVISO SWH observation in April 2017 is less than 0.03 m. The RMSE is about 0.3 m, and the correlation is highup to 0.92.
# Navigation Scenario Permutation Model for Training of
## 3 Results
### Distribution Application of Selected Navigation Elements
#### 3.2.2 Proportion Difference in Navigation Elements  
The number of components was set as eight, and the representative values in Table 2 were designated for the eight components considering the proportion. The proportion of each division is visualized in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Divisions**} & \multicolumn{3}{c}{**Course Altering Angle at Waypoint**} & \multicolumn{3}{c}{**Distance between Waypoints**} \\  & **Range of Value** & **Representative** & **Proportion** & **Range of Value** & **Representative** & **Proportion** \\  & **(deg)** & **Values (deg)** & **(\%)** & **(NM)** & **Values (NM)** & **(\%)** \\ \hline
1 & 0–17 & 9 & 48.8 & 1–4 & 2 & 65.0 \\
2 & 18–35 & 26 & 32.8 & 4–8 & 6 & 18.6 \\
3 & 36–53 & 40 & 13.7 & 8–12 & 10 & 8.2 \\
4 & 54–72 & 63 & 3.8 & 12–16 & 14 & 4.7 \\
5 & 73–90 & 81 & 0.5 & 16–20 & 18 & 3.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Element value range divisions and representative values.  
Figure 8: The proportion of divisions for each selected navigation element.  
Figure 7: Distribution of “distance between waypoints” element.
# Efficacy Improvement of Anomaly Detection by Using Intelligence Sharing Scheme
## 5 Experimental Evaluation and Discussion
### Improvement of Per-Flow Anomaly Scores
Based on the considerations in Section 1.2, one would think that a primarily small-world network [44] would exhibit a low AUC score: AUCs would be expected to be correlated with the amount of network traffic because small-world networks have less information to train their classifiers on. Surprisingly, this is not the case as illustrated in Figure 9. Similarly, AUC improvement by the reputation tables was not visibly correlated with network size. Even big networks [45] can obviously exhibit bad classification characteristics and benefit from the improvement by global reputation tables.
# Benefits and Impact of Joint Metric of AOA/RSS/TOF on Indoor Localization Error
## 4 Simulation Results
### Impact of AP Number
#### 4.1.1 Performance of Metric of Single Measurement  
Figures 3 and 4 illustrate the sum of diagonal elements in the FIM, which represents the Fisher information with respect to the metrics of RSS, TOF and AOA and the corresponding error bounds. In Figure 3, the metric of AOA is verified to be able to provide much more Fisher information compared with the metric of RSS or TOF. In addition, the larger number of APs results in the higher Fisher information as expected. Figure 4 shows that the error bound increases consistently as the variance of the metric of RSS, AOA or TOF increases. Furthermore, we also find that the smaller variance of the metric of RSS, AOA or TOF corresponds to the higher Fisher information and lower error bound in indoor localization.
## 3 Reliability Models of WSNs
### Basic Blocks  
The reliability of communication links (block _Link_) and WSN nodes (block _Node_) are modeled by using RBD blocks as shown in Figure 7.  
The reliability of a node is defined as a sequence of the following blocks: application (_App_), operating system (_OS_), middleware (_Middleware_), platform (_Hardware_), radio (_Radio_) and battery level (_Battery_). As the reliability of a WSN node is composed by the reliability in series of their components, if one of them fails, the whole node fails (see Section 2.3). Each block has associated a  
Figure 6: Overview of creating the Region Model.  
Figure 7: Basic blocks of the reliability model.  
reliability defined by the user, obtained through simulation, or defined in the software/hardware specification.  
In fact, the simulation is used to know the status (e.g., battery level, radio active/inactive) of a component at time \(t\). By using this information (status), we define the component's reliability. For example, the node reliability is directly affected by its battery level, _i.e._, low battery level means low reliability. Over time, the battery is consumed and can reach a level that is unable to meet the energy requirement of the node, which will not work properly (higher probability of fail) or will die (always fail). To estimate the battery reliability, its status is simulated to yields its reliability as defined in Section 4.1.  
Similarly, the reliability of the radio is also defined by simulation. The radio status assumes two values: on and off. The node can send, receive or forward a packet when the radio is turned on. However, when it is turned off, the node becomes \"inactive\" and cannot send, receive or forward packets. A packet is lost if it is forwarded to a sensor node with radio turned off. To represent this situation in RBD, the block _Radio_ should be used to indicate whether radio is on (reliability \(>0.0\)) or off (reliability \(=0.0\)) and its status at time \(t\) can be captured via simulation.
## 2 Testing and Evaluation Methodology
### Smart System Testing
#### 2.3.2 Smart System Field Testing  
In this study, three prototypes were developed. The testing and evaluation activities were done with all the three prototypes during 2015-2016. Although testing of the smart system was done during different stages of the development of the system, this report presents only the testing and evaluation results of the final smart system prototype used by end users during final testing activities. The smart boxes were used at farm-to-storage and storage-to-consumer transport stages.
# Impact of High Water Levels in Lake Baikal on Rare Plant Species in the Coastal Zone
## 2 Materials and Methods
### Inundation Modelling  
The most important step in in inundation modeling is the selection of a digital elevation model (DEM). The accuracy of global DEMs, including those derived from the Shuttle Radar Topographic Mission (SRTM), the Advanced Land Observing Satellite (ALOS), and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), among others, typically range from a few meters in height to tens of meters in plane. This level of accuracy is insufficient for determining inundation zones when the level of Lake Baikal changes from 457.0 to 457.85 m. The scarcity of large-scale maps was addressed through the implementation of SfM (structure-from-motion) technology to obtain DEMs [35; 36]. For this purpose, digital aerial photography of key areas with rare plant species was carried out according to [37]. Digital aerial photography was carried out by using a DJI MavicPro unmanned aerial vehicle (UAV) from an altitude of 150 m. The image pixel size on the terrain at the selected flight altitude does not exceed 9 cm, which corresponds to the scale of the topographic plane M 1:1000. At least 70% of the endlap and sidelap of images were ensured. Georeferencing was performed using a network of ground control points (GCP), with black and white squares clearly visible on the images serving as the GCPs. GCP georeferencing was performed using two dual-frequency GNSS (global navigation satellite system) receivers operating in Real Time Kinematic mode with georeferencing to points of the National Geodetic Network. The GCP coordinates and elevations were determined with the mean square error of measurement 2.5 mm \(\pm\) 0.3 mm/km in plane and 5 mm \(\pm\) 0.5 mm/km in elevation.  
The processing of aerial images in Agisoft Metashape Professional 2.0 [38] software resulted in digital elevation models. The modeling of inundation zones as a result of Lake Baikal water-level fluctuations was carried out. These zones were determined in the ranges 455.54-456.00 and 457.00-457.85 m with a ten-centimeter step. The boundaries of the intersection of the water surface with the terrain relief were determined at each level employing the \"contour list\" tool of the ArcGIS Desktop 10.8. Consequently, vector polygons were obtained.
# Biogenic Nanoparticles: Synthesis, Characterisation and Applications
## 2 Biogenic Synthesis of Nanoparticles
### Characterisation of Nanoparticles
#### 2.5.3 Magnetic Properties  
Iron-based nanoparticles have gained popularity over recent years; therefore, the study of their magnetic properties is necessary. Some of the techniques include electron  
\begin{table}
\begin{tabular}{l l} \hline
**Techniques** & **Characteristic** \\ \hline Atomic force microscopy (AFM) & Geometry, structure, distribution \\ Differential scanning calorimetry (DSC) & Thermal performance \\ Dynamic light scattering (DLS) & Hydrodynamic size distribution \\ Fluorescence spectroscopy & Optical properties \\ UV-VIS/Infrared spectroscopy & Structure, Functional group analysis \\ Mass spectrometry (MS) & Molecular weight, composition, surface \\ Nuclear magnetic resonance (NMR) & Structure, composition, purity \\ Scanning electron microscopy (SEM) & Aggregation, size and shape \\ Transmission electron microscopy (TEM) & Aggregation, size and shape \\ X-ray photoelectron spectroscopy (XPS) & Elemental and chemical composition \\ \hline \end{tabular}
\end{table}
Table 4: List of techniques for characterisation of nanoparticles.  
Figure 2: (**a**) Transmission electron microscopy (TEM) image showing bacterial synthesised AuNPs and (**b**) histogram showing particle size distribution with the average size being 20.93 \(\pm\) 3.46 nm [43].  
paramagnetic resonance (EPR), vibrating-sample magnetometer (VSM) and superconducting quantum interference device (SQUID). EPR is used to detect and identify free radicals and paramagnetic centres in chemicals. It allows for the investigation of the physical properties of magnetic nanoparticles and the influence of the external magnetic field by means of interaction with electrons in a sample. For highly sensitive magnetic measurements, VSM and SQUID are employed, having sensitivities of \(10^{-6}\) emu and \(10^{-10}\) emu, respectively. SQUID is employed to characterise media such as powder, thin films and other solid and liquid samples.
## 1 Introduction  
In the rapidly evolving automotive industry, the design and validation of car infotainment systems pose significant challenges [1; 2]. Traditional methods rely heavily on physical prototypes, which are time-consuming and costly [3], often requiring an initial concept to pass through multiple stages (such as mockup creation and real hardware installation) and departments (including 3D printing, quality control, and field testing) to gather feedback and identify possible issues. To address these challenges, the V-Cockpit project aims to improve this process by reducing dependencies and constraints connected to physical equipment and the real world through the use of virtual reality (VR) technology. By integrating real and simulated hardware along with behavioral analysis algorithms, V-Cockpit seeks to enhance the productivity of the design and prototyping phases associated with developing the car console and its interface with the in-vehicle infotainment (IVI) system.  
The primary objective of the V-Cockpit project is to develop a VR system using digital twins to shorten the design validation time and reduce the associated costs. Although precise figures are challenging to predict [4], the project advances the sector by improving decision-making and reducing the non-recurring expenses (NREs) related to bringing a new IVI system to the market.  
By running the IVI software on its native platform within the VR environment, V-Cockpit ensures compatibility with embedded hardware constraints, thereby enhancing the flexibility and applicability of the design process.  
In this technical note, we provide an overview of the V-Cockpit platform's system architecture, implementation, and anticipated benefits.  
This note's main contributions are as follows:  
* We introduce the V-Cockpit project and offering a high-level perspective on the platform as a whole. Future publications will explore specific aspects in greater detail and reference this note for context.
* We demonstrate how a typical cockpit design loop can be executed using our platform by splitting it into three representative use cases.
* We present an initial analysis of the benefits and potential of our approach as evidenced by its application across these three use cases.  
The following sections will examine the context and state of the art related to the topic, detail the materials and methods used to develop the V-Cockpit platform, present the results achieved, and discuss their implications for the future of car infotainment system design, before concluding with recommendations for potential future research.