esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Multi-Antenna Techniques for V2X Communications--Prototyping and Experimentation
## 2 Distributed Multiple Antennas for V2X Communications
### Configuration of the ESPAR Antenna and Performance
The omni-directional radiation pattern of the 5-wire monopole ESPAR is more symmetrical. However, the integration of the 5-element ESPAR in the upper plastic mirror arm was considered to be impossible due to its large dimensions. Figure 3 depicts the four measured gain patterns of the 3-printed monopole antenna, omnidirectional (omni) and directive in left and right side respectively (in both H-plane/top and E-plane/bottom). A significant 3-4 dB gain increase is observed from the OFF-OFF to the ON-OFF antenna state. Figure 3: The measured 5.9 GHz gain radiation patterns of the ESPAR antenna for the quasi-omni (left) and the directive states (right) at the H-plane (top) and the E-plane (bottom). Figure 2: The simulated 5.9 GHz H-plane radiation patterns of the 3-printed ESPAR antenna (blue) and the 5-wire monopole ESPAR antenna (red) for the two antenna states ((a) OFF-OFF, (b) ON-OFF).
# Aveiro Canyon Head (Portugal) Submarine Slope Instability Assessment
## 3 Methods  
Two statistical methods were used in this study, in order to assess the canyon slope instability: the bivariate information value method (IV) [23] and the multivariate binomial logistic regression method (LR) [24; 25]. Both methods consider the relations between the occurrence of a given event or state, in this case the occurrence of a landslide, with factors which may have relations with its susceptibility or triggering.  
These methods are data-driven methods that statistically evaluate the combination of the conditioning/predisposing factors that are better related to the spatial distribution of past and present instabilities [26]. This type of analysis assumes that the factors that conditioned the past and present instability will produce the same effects in the future [26; 27; 28; 29]. Therefore, it is possible to make a quantified prediction of susceptibility in as yet non-failing areas [26; 30; 31].  
Due to the available data in this area, the possible predisposing factors were mainly derived from the hydrographic information (bathymetry) and sediment cover. The considered factors were bathymetry, geometric parameters obtained on the bathymetric surface (mean slope angle, slope face direction (aspect) and seafloor curvature) and superficial sediments mapping. These predisposing factors are also considered to contribute to the occurrence of landslides in slope instability assessment studies (e.g., [19; 32; 33; 34; 35]).  
In summary, this approach requires the production of an inventory of recognized slope movements in the study area; the identification and mapping of the predisposition factors; a comparison between the inventory of movements and the predisposition factors; the production of a susceptibility map; and the model validation.
# Detecting Plasma Detachment in the Wendelstein 7-X Stellarator Using Machine Learning
## 2 Methods
### Performance Evaluation Metrics  
Several metrics can be used for measuring the performance of classification models. The first and foremost step to acquire these metrics is to calculate the confusion matrix of the predictions. Each row of the matrix represents the instances in a predicted class, while each column represents the instances in an actual class as seen in Figure 5.  
First we define Precision as \(\frac{TP}{TP+FP}\) and Recall as \(\frac{TP}{TP+FN}\), by calculating the harmonic mean of these quantities we get the \(F1\) score:  
\[F1=2\cdot\frac{Precision\cdot Recall}{Precision+Recall}=\frac{TP}{TP+0.5(FP+ FN)} \tag{1}\]  
Figure 4: Detachment detection workflow. 1. The EDICAM camera system takes images in the vessel. 2. We extract the appropriate intensity profiles from the images. 3. We use the profiles as inputs to a machine learning models to determine detachment or non-detachment. Greatly simplified schematics of the (a) Logistic Regression model (b) the Random Forest model.  
Figure 5: Schematics of the confusion matrix (**left**). Schematics of the Receiver Operating Characteristic Curve (**right**). The meaning of the used abbreviations; \(TN\): True Negative, \(FP\): False Positive, \(FN\): False Negative, \(TP\): True Positive.  
The \(F1\) score takes into account how the data are distributed, thus is accurate for both balanced and unbalanced datasets. The Receiver Operating Characteristic Curve (ROC Curve) illustrates the performance of a binary classification model as its discrimination threshold is varied. We create the ROC curve by plotting the recall (or true positive rate--TPR) as a function of false positive rate (FPR), where \(FPR=\frac{FP}{FP+TN}\), for each examined threshold value. By integrating the curve, we get the Area Under the ROC Curve (AUC) metric, which shows us the ability of the model of separating the two classes. AUC varies between 0 and 1, with a random classifier yielding 0.5.
# Experimental Study on Cyclic Shear Performance of the Four-Way Geogrid Reinforcement-Soil Interface
## 3 Results and Discussion
### Shear Stiffness and Damping Ratio
#### 3.2.2 Effect of the Shear Displacement Amplitudes  
Figure 11a illustrates the variation curves of the shear stiffness of the geogrid-soil interface at different shear displacement amplitudes under a normal stress of 120 kPa. The results indicate that when \(A_{w}\) = 0.5 mm, the shear stiffness exhibits shear hardening with the number of cycles, while for \(A_{w}\) > 0.5 mm, the shear stiffness shows the phenomenon of softening with the number of cycles. The grating is more susceptible to wear and deformation when the shear amplitude is relatively large. This wear and deformation can lead to a degradation of the interface shear strength. As the shear displacement amplitude increases, the corresponding shear stiffness decreases, which is consistent with the conclusion reached by [PERSON] et al. [32] at the same number of cycles. As the number of cycles increases, the shear stiffness for different shear displacement amplitudes eventually converges to a stable value. Figure 11b displays the variation curves of the damping ratio of the geogrid-soil interface for different shear displacement amplitudes. The damping ratio exhibits cyclic shear hardening as the shear displacement amplitude increases. When the shear displacement is small (\(A_{w}\) = 0.5 mm), the damping ratio curve does not show significant fluctuations with the number of cycles. However, as the shear displacement increases (\(A_{w}\) > 0.5 mm), the damping ratio curve becomes more irregular. The damping ratio increases as the shear displacement amplitude increases with the number of cycles. The larger the damping ratio, the faster the rate of energy dissipation at the geogrid-soil interface. Similarly, the larger the shear displacement amplitude, the faster the rate of energy dissipation at the geogrid-soil interface [33].  
Figure 11: Shear stiffness and damping ratio under different shear displacement amplitudes. (**a**) Shear stiffness; (**b**) Damping ratio.  
Figure 10: Shear stiffness and damping ratio under different normal stress. (**a**) Shear stiffness; (**b**) Damping ratio.
# Ground Control System Based Routing for Reliable and Efficient Multi-Drone Control System
## 4 Performance Evaluation
### GCS-Routing Implementation  
We used F550 ARF KITs from DJI as drone frames and assembled them ourselves, as shown in Figure 9. We adopted ODROID-U3 of HardKenel for drone computing and networking operations, and Pixhawk autopilot [38] of 3D Robotics as the flight controller. ODROID-U3 and Pixhawk were connected via USB to a UART module.  
We used two ipTime N600 UA modules as communication interfaces. The following section details experiments where drones were connected with ad-hoc network using IEEE 802.11g as the primary interface. Each drone could act as an AP using a secondary interface to provide network to users located in the service area of the drone. The secondary interface also used IEEE 802.11g, but used a different channel from the ad-hoc network channel to avoid influencing routing performance. We implemented GCS-routing using C on ODROID-U3 with Linux network libraries, and used _Babeld_ and _OLSRd_ as DSDV and OLSR protocols, respectively. These protocols were implementations of RFC 6126 [39] and RFC 3626 [18], respectively. We built six drones and implemented GCS with C on a laptop computer running Linux.
# Evaluation of Viscoelastic and Rotational Friction Dampers for Coupled Shear Wall System
## 1 Introduction
The hybrid damper's mechanism begins with the shear deformation of the VE component, which dissipates energy through viscous hysteresis under small to moderate displacements. As the applied load increases, the resulting moment at the damper's joints reaches a critical threshold, triggering the activation of the RF dampers. This sequential engagement ensures efficient energy dissipation: the VE damper handles low-amplitude vibrations, while the RF damper provides supplemental resistance under extreme displacements, creating a two-stage energy dissipation system that optimizes performance across varying demand levels. In this paper, as a first step, experiments and analyses are conducted separately for the VE damper and the RF damper to verify their effectiveness. First, the hysteretic behavior and damping effectiveness of each damper are examined through element tests and their numerical modes are constructed. Then, large-scale shake table tests of the frame with coupled shear walls and the damper are conducted to evaluate the effectiveness of the damper under the white noise, sinusoidal, and Kokuji wave excitations. Finally, the accuracy of the numerical model is verified by comparing the experimental results with the numerical results.
## 2 Background and Related Work
### Grammar-Guided Genetic Programming  
While numerous GP systems exist, G3P demonstrates outstanding efficacy. G3P, a variant of GP, utilises grammar as its representation. Notably, two well-known variants within this framework are grammatical evolution [45] and context-free grammar genetic programming (CFG-GP) by [PERSON] [46]. What sets G3P apart is its reliance on grammar as a guiding principle throughout program evolution, ensuring syntactically valid programs at each stage. Grammars offer adaptability and the ability to define search spaces externally to the GP system. Consequently, they find applications in diverse domains, including automated programming [47], transport system management [48], and wireless communications scheduling [49; 50; 51; 52; 53]. [PERSON] et al. [28] proposed a G3P system featuring a composite and self-adaptive grammar to address synthesis problems. This innovation overcomes the limitation of customising or adjusting grammars for each problem. The approach involves predefined short grammars, each associated with a specific data type that describes the function or program to be evolved. By reusing these grammars across diverse problems, G3P reduces the computation costs while excluding data types that are irrelevant to the problem. Furthermore, subsequent enhancements in [54] extend the predefined grammar to include data types for characters instead of handling them as strings. It also added recursions to the grammar, enabling diverse program structures in the output program.
## 2 Edge Computing and Related Computing Paradigms
### Edge Computing
#### 2.4.2 Mobile Ad Hoc Cloud Computing  
The other option to enable cloud computing at the edge is doing computing directly at the UEs through an ad hoc cloud that allows several UEs in proximity to combine their computation power; therefore, they can process high-demanding applications locally [43, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59].  
MCC is pervasive but is not very suitable in the absence of a centralized cloud or infrastructure. An ad hoc mobile network is an impermanent and dynamic network of nodes that has been created via routing and transport protocols. Mobile ad hoc cloud computing (MACC) is the most decentralized form of a network [60]. In MACC, mobile devices create a highly dynamic network topology; network adaptation for more devices to frequently leave/join the network is necessary. Ad hoc mobile devices are also able to create clouds to use for networking, storage, and computing. Unmanned vehicular systems and group live video streaming are examples of MACC use cases.  
Compared with cloudlets, virtualization with VM capability configuration is required in cloudlets, but such infrastructure is not required in MACC. Mobility has been supported in both cloudlet computing and MACC; however, real-time IoT applications are not supported in resource-constrained MACC. Tasks in the cloudlet have been divided among cloudlet nodes placed in the vicinity of mobile devices in order to bear local services for mobile customers [61].  
Resources in MACC have an ad hoc nature; therefore, MACC is basically distinct from cloud computing. Mobile devices in MACC act as data storage, data providers, and processing devices, and because of the lack of network infrastructure, they also control routing traffic amongst themselves. Local mobile resources have been pooled to create an ad hoc cloud; therefore, MACC can offer high computation. These attributes are distinct from the target users, connectivity, and architecture in cloud computing.  
The service access method, hardware, and distance from users are different in MACC and MCC. The computation in MACC happens on mobile devices; however, in MCC, the computation is away from mobile devices. In MACC, only mobile devices have been required to operate; however, in MCC, extensive data centers used for cloud computing are also needed. Therefore, computation power in MCC is high, but latency is also high in MCC. Security in MACC is only achieved through mobile devices but achieving trust can be challenging in the absence of a secure collaboration framework. Services in MACC are only accessible using mobile devices; the connection between mobile devices is through Bluetooth, Wi-Fi, or other cellular protocols.  
A mobile ad hoc network (MANET) is a similar concept to MACC. In MANETs, mobile host devices are connected to each other with a single hop and without base stations [62]. MANET devices create dynamic networks but not necessarily a cloud, meaning that the computation or storage resource pools are not necessary for MANETs. Solutions such as redundancy and broadcasting to MANETs are applicable to MACC.
# Research Developments of Aerostatic Thrust Bearings: A Review
## 2 Theoretical Models and Experiment Methods
### Theoretical Model
#### 2.1.1 Gas Flow in ATBs  
In the initial study, many scholars assumed the gas flow in the bearing was laminar. It was gradually found that the actual gas flow was much more complex. The gas film gap is extremely tiny compared with the bearing size, in the order of 1/1000, which leads to complex flow in the bearing. The gas flow in ATBs has been investigated by many scholars. [PERSON] studied the pressure distribution in the gas clearance of the parallel disk ATBs [11]. There was a sharp pressure drop at the outlet of the orifice of the aerostatic bearing. The pressure distribution in the gas film did not drop steadily from the outlet of the orifice to the outlet of the bearings. It no longer followed the Reynolds lubrication equation. An empirical model was introduced to predict this phenomenon and showed a good agreement with experimental data [16]. The pressure variations in the region around the inlet were accurately predicted using this model. In addition, the feeding system is closely related to the type of restrictor. The flow characteristics through feeding systems become more complicated considering the orifice shape. In order to predict the bearing performance more accurately by solving the Reynolds equation, it is necessary to know the flow characteristics and downstream pressures of the feeding system [17].
# A Study on the Evaluation Method of Highway Driving Assist System Using Monocular Camera
## 2 Proposed Formulation for HDA System with Monocular Camera  
The HDA system is activated by utilizing GPS data when a vehicle enters a highway. This facilitates the recognition of the lanes and lead vehicles to ensure safe following, distances, and lane-keeping. The distance to the lead vehicle is maintained via longitudinal control, and lane keeping is achieved via lateral control.
# Broadband Plasmonic Metamaterial Optical Absorber for the Visible to Near-Infrared Region
## 3 Results Analysis
### Mechanical Stress  
The MA can get curved under physical stress as the structure is a very small-scale nanoarchitecture. The absorption characteristics of the MA has been investigated in various banding situation. Figure 12 shows the absorption behaviour of the MA as it bends towards the inside (Concave Bending). Here R is the bending diameter, and \(\theta_{1}\) is the acquired central angle for a single unit cell. Figure 12b shows the absorption behaviour of the MA as it bends towards the outside (Convex Bending). Here \(\theta_{2}\) is the acquired central angle for a single unit cell. The proposed MA's absorption behaviour is more stable when it bends towards the inside. Overall bandwidth and peak absorption remain almost the same. But its losses its stability as it bends towards the outside. Also, overall bandwidth and peak absorption decrease rapidly.
###### Abstract
We compare these approaches in the context of their reconfigurability, and in particular, how cost effectively they modify or adapt to meet new or changed requirements; to perform this comparison, we develop two new metrics: (1) an efficiency/cost based metric; and (2) a performance-based metric. As well, we compare the tracking performance of these approaches using a tracking accuracy metric. Figure 1: Mobile Node tracking in a factory wireless sensor network (WSN). In the next section, we provide a summary of related work on wireless sensor node localization and tracking. Next, we describe the WSN architecture and the clustering approaches explored in this research. In Section 4, we present our experiments with these clustering approaches then provide our conclusions and future work in Section 5.
## 4 Results
### Comparative Analysis
#### 4.3.2 Comparison of NCA-F on ADNIMERGE with Literature  
Three different categories of data are used to test the NCA-F method: Neuroimaging, Cognitive, and a combination of both. NCA-F fares the best when a combination of both features is used, as in the ADNIMERGE dataset, where the 7 and 12 best features are selected from neuroimaging and cognitive features, respectively. A reported accuracy of 83.42% is achieved, in contrast, which drops to 74.33% if only neuroimaging data are used with four features. Similarly, the best 18 cognitive features correspond to an accuracy of 83.15%, a mere drop of 0.27% in terms of accuracy. The frequency of recording remains static for all the experiments, at 13,892. However, it is the tradeoff required to opt between enhanced performance and higher accuracy.
# Optimisation of 2D U-Net Model Components for Automatic Prostate Segmentation on MRI
## 3 Materials and Methods
### Optimised Network Architecture  
The configuration of the optimised network, model UNet_S.2.0.1.1, is shown in Figure 5. The network starts with a \(320\times 320\times 1\) input layer followed by a batch normalisation layer. Each convolution block consists of \(3\times 3\) convolution layers, batch normalisation layers and ReLU activation layers. A skip connection with a concatenation is used to pass the feature maps between the outputs of the activation layers in a convolution block so as to combine the feature maps. The concatenated feature maps are passed deeper into the encoder as well as to the decoder to improve the spatial information in the higher level feature maps. A \(2\times 2\) average pooling layer is used to downsample the feature maps from a resolution of \(320\times 320\) to \(20\times 20\). A dropout layer, with a dropout rate of 0.5, is applied after the fourth and fifth convolution blocks. The Interp+Conv layers consist of a nearest neighbour interpolation followed by \(2\times 2\) convolution, batch normalisation and ReLU activation layers. The final classification layer consists of \(1\times 1\) convolution, batch normalisation and sigmoid activation layers.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Phase**} & \multirow{2}{*}{**Network**} & \multicolumn{6}{c}{**5-Fold Cross-Validation DSC (\%)**} \\ \cline{3-8}  & & **Fold 1** & **Fold 2** & **Fold 3** & **Fold 4** & **Fold 5** & **Avg** \\ \hline \multirow{4}{*}{\(1\)} & **UNet\_S** & 85.28 & 86.86 & 83.36 & 86.16 & 81.53 & **84.64** \\ \cline{2-8}  & **UNet\_S**1 & 85.26 & 83.04 & 79.66 & 81.43 & 79.16 & 81.70 \\ \cline{2-8}  & **UNet\_S**2 & 85.07 & 85.25 & 80.20 & 81.18 & 80.84 & 82.51 \\ \hline \multirow{4}{*}{\(2\)} & **UNet\_S**1 & 84.40 & 86.30 & 80.29 & 80.49 & 82.98 & 82.89 \\ \cline{2-8}  & **UNet\_S**1.1 & 83.37 & 84.22 & 78.04 & 82.05 & 79.35 & 81.41 \\ \cline{2-8}  & **UNet\_S**2.1 & 83.70 & 82.53 & 79.19 & 82.68 & 82.03 & 82.03 \\ \cline{2-8}  & **UNet\_S**2 & 85.46 & 88.01 & 82.91 & 84.76 & 84.89 & **85.21** \\ \cline{2-8}  & **UNet\_S**1.2 & 85.43 & 84.67 & 79.06 & 80.60 & 83.13 & 82.58 \\ \cline{2-8}  & **UNet\_S**2.2 & 85.28 & 81.63 & 81.08 & 84.35 & 80.33 & 82.53 \\ \hline \multirow{2}{*}{\(3\)} & **UNet\_S**2.1 & 82.31 & 84.97 & 80.11 & 84.08 & 81.47 & 82.59 \\ \hline \multirow{2}{*}{\(4\)} & **UNet\_S**2.0.1 & 84.33 & 87.26 & 82.80 & 88.79 & 84.73 & **85.58** \\ \cline{2-8}  & **UNet\_S**2.0.1 & 84.02 & 88.87 & 83.97 & 89.23 & 85.07 & **86.23** \\ \cline{2-8}  & **UNet\_S**2.0.1 & 84.55 & 84.71 & 81.36 & 87.38 & 83.98 & 84.40 \\ \cline{2-8}  & **UNet\_S**2.0.1 & 84.12 & 87.55 & 82.80 & 88.92 & 84.17 & 85.51 \\ \hline \multirow{2}{*}{\(6\)} & **UNet\_S**2.0.1 & 81.68 & 83.89 & 79.45 & 88.06 & 84.00 & 83.41 \\ \cline{2-8}  & **UNet\_S**2.0.1 & 84.64 & 87.95 & 82.68 & 87.75 & 84.68 & 85.54 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model architecture optimisation results.
## 7 Experiments and Results Analysis  
We set the body area network node set \(V=\{v_{0},v_{2},v_{3},\ldots,v_{150}\}\) for the simulation environment, the data sending node is \(v_{0}\), and the sink node is \(v_{150}\); the node remaining energy \(c_{i}\in\{1,2,\ldots,50\}\); the node service factor \(\alpha_{i}\in\{1,2,3\}\); \(E=\{e_{1},e_{2},e_{3},\ldots,e_{1525}\}\) is an edge set. When \(\lessdot i,j\succ E\), node \(i\) transmits a packet to node \(j\), energy consumption \(a_{ij}\in\{1,2,3\}\). In order to facilitate the verification result, it is assumed that the node topology is a hierarchical network, and each layer node is only connected to the upper layer and the next layer. The adjustment parameters for setting the fitness function are: \(\alpha\) = 50, \(\beta\) = 5, \(\gamma\) = 0.1.  
We implemented the OEABC algorithm in C++ programming and tested it in the above simulation environment. In our simulation configuration, we adopt the following simulation settings: the data collection node is \(v_{0}\) and the sink node is \(v_{150}\). The communication link is established between neighbor nodes, and each node has 10-15 predecessors and successors. The data packet is sent out from \(v_{0}\), the data packet is exchanged by the successor node, the transmission path from the data collection node to the network sink node is established, and the data packet is sent to the sink node \(v_{150}\). The main simulation parameter values are listed in Table 2 below.