esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 SQI and RAIM Interaction
### GDOP Control
It means that a faulty situation is detected, but it is impossible to identify which is the faulty satellite and consequently RAIM cannot perform the exclusion. In this situation the RAIM algorithm is not able to provide information related to Vertical or Horizontal Protection Level (VPL and HPL). Figure 14: Example of global (**a**) and local (**b**) test assisted by SQI information on scenario 1 without unreliable solutions. Figure 13: Example of global (**a**) and local (**b**) test on scenario 1 with two unreliable solutions. Figure 13 shows test results regarding scenario 1 with 9 satellites in view, without using SQI, with a false alarm probability \(\alpha=3.33\times 10^{-5}\) and missed detection probability \(\beta=1\times 10^{-2}\). The graph on top is the evolution in time of the global test statistic compared to the global threshold. Then, the local test is performed on the points above the global threshold. In the graph on bottom, the evolution in time of the local test statistic shows that there are only two points under the local threshold (circle points). These values under the local threshold are the navigation solutions which fail the global test, but pass the local one. By using SQI, we want to identify the possible biased PR and exclude it to make reliable the navigation solution. To do this, after the global test failure and when local test is valid, instead of declaring an unreliable solution, we can check the SQI values for each satellite and decide to exclude the satellite with the lowest SQI and repeat again the global and local test (if needed). In Figure 14 the results of the assistance are shown. We observe that there are not points under the threshold in the local test graph, since after the exclusion, the global test is repeated and no more biases are detected and so the local test is not repeated.
# Pressure-Reducing Design of 3D-Printed Diabetic Shoe Midsole Utilizing Auxetic Lattice Structure
###### Abstract  
With the global rise in the prevalence of diabetes, diabetic patients need innovative footwear designs to reduce the risk of foot ulcers. This study examined the mechanical properties of diabetic shoe misdoseles featuring auxetic lattice structures. Through the construction of finite element models and simulation, this research compared the biomechanical parameter differences in the plantar regions of the metatarsal head, midfoot, and hindfoot when wearing two types of auxetic midsoles with internal angles of 60\({}^{\circ}\) and 75\({}^{\circ}\) and a non-auxetic midsole with an internal angle of 90\({}^{\circ}\) under both walking and running conditions. Compared to the non-auxetic midsole, the auxetic midsoles significantly reduced the peak plantar pressure and optimized the pressure distribution across various plantar regions. Notably, the auxetic 60\({}^{\circ}\) midsole reduced the peak plantar pressure by 19.68-55.25% and 16.19-54.39% compared to the non-auxetic 90\({}^{\circ}\) midsole during walking and running, respectively. This study also verified that the auxetic midsoles exhibited greater adaptability and compliance to the plantar foot shape, contributing to reductions in plantar pressure in comparisons of deformation values and plantar contact areas across the different midsoles. Auxetic midsoles manufactured using 3D printing technology have significant potential to prevent diabetic foot ulcers and maintain human foot health. This research integrates insights and techniques from materials science and ergonomics, offering a new direction for footwear design.  
diabetic footwear; auxetic lattice structures; foot biomechanics; finite element analysis; 3D-printed shoe midsole
# Automated Detection of Greenhouse Structures Using Cascade Mask R-CNN
## 3 Materials and Methods  
### Area of Study and Data  
This study utilized 2054 aerial images of restricted development zones in the Republic of Korea. The Ministry of Land, Infrastructure, and Transport (MOLIT), which is a government agency of the Republic of Korea, provides data on restricted development zones designated throughout the country in Shapefile (SHP) format [35]. We can construct 2054 aerial images based on the retrieved dataset, including restricted development zones.  
The dataset comprised a set of aerial photographs obtained from three big cities (e.g., Hanam, Hwaseong, and Gunpo) of the Republic of Korea, including two types of object information, such as greenhouse facilities and residences. The cameras for capturing all three cities have the same resolution of 25 cm. All aerial images are 410 \(\times\) 410 pixels in size, including various environments, such as forests, urban areas, roads, and agricultural landscapes. In the Republic of Korea, restricted development zones are designated to preserve the green environment necessary for the health of citizens, so the environment is not very different in different cities. Therefore, there was no significant difference between the three cities. Accordingly, out of 2054 images containing data from three cities, 1643 randomly selected aerial images (80%) were used to train and validate the proposed model, and 411 images were used for model evaluation. The ground-truth values required for model training and evaluation were created using the VGG Image Annotator (VIA) tool [36] to create a mask of houses and greenhouses appearing in aerial images (Figure 1).  
This study used Amazon Web Service (AWS) to implement our proposed model, which provided high-performance computations. The overall system architecture of our study is presented in Figure 2. First, aerial image data were uploaded to an AWS S3 bucket, and then the data were preprocessed using the Python programming language. The Cascade Mask R-CNN used in this study was implemented using TensorFlow and the Keras framework. Model learning and performance evaluation were conducted in environments of p2-xlarge (61 GB memory and a graphics processing unit (GPU) with 11,441 MB) and CUDA version 10.0.
# Parametric Selection of Optimized Epicyclic Gearbox Layouts for Wind Power Plant Applications
## 4 Results
### Data Presentation  
The most important parameters of every gear train have been listed in the tables:  
* Scheme and layout variant (S/V);
* Ideal torque ratios \(t_{\rm I}\) and \(t_{\rm II}\);
* Transmission ratio \(i\);
* Gearbox mass \(m\);
* Gearbox efficiency \(\eta\);
* Larger ring diameter \(d_{\rm 3 max}\);
* Ring diameter ratio \(d_{\rm 3 max}/d_{\rm 3 min}\);
* Gear train modules \(m_{\rm I}\) and \(m_{\rm II}\).  
The resulting gear trains were synthetized and ranked according to the criteria of minimal mass, minimal larger ring gear reference diameter size, and maximum efficiency. The criteria of minimal mass and maximum efficiency are self-explaining, while the minimal larger ring gear reference diameter is the criterion required for technological assessment. Both positive and negative transmission ratios are acceptable as the direction of rotation of the generator is irrelevant [59]. The gear trains were ranked separately for positive and negative transmission ratios for clarity. Only gear trains having efficiencies above 90% were considered feasible and passed on for further examination.
## 2 Materials and Methods
### Visibility Observations  
To allow for a remote observation of visibility, a high-resolution camera for the visible spectrum was installed at LZTT, where the prevailing visibility in METAR messages is reported every 30 min by professional observers. The visible camera installed on a rotator sends 8 images of the horizon covering all the cardinal directions (N, NE, E, SE, S, SW, W, and NW) to the system. Based on these images, the proposed software provides an easy-to-use HMI and tools for the remote observation of the prevailing visibility as close to the estimation of the local observer as possible.  
Markers with distance labels for all directions represent an aiding tool for the remote observer. These markers were selected carefully to cover the variability of distances in each direction. The number of markers is normally superior to the number of local observations, and thus, it makes the observation of the prevailing visibility more precise. Table 1 presents a basic overview on the marker settings at LZTT. The marker counts were divided into four intervals that are being used by the ICAO [1]. The observations evaluated by means of the proposed system are based on a multitude of markers used by local observers and this may lead to differences in statistics.  
By switching markers between the statuses visible/not visible, the visibility in each direction and thus, the prevailing visibility can automatically be re-calculated. Beyond this, once the visibility markers have been set, they are available for any later verification or correction by the remote observer (Figure 6). One can easily access the current images, history of images, or images related to ideal conditions for a quick comparison to improve the observer's decision on the visibility. The system settings also support generating METAR messages, where all the fields are automatically filled in and the observer only edits unautomated fields or fields with a correct suggestion from a sensor. The prevailing visibility determined through the remote observer's HMI is also pre-filled in the METAR generation tool.  
For a possible comparison of the remote observations with the METAR messages, the remote observations were timed 10 min before METAR was issued, as according to local references, this is the approximate time when local observations should take place. Generally, there is no obligation for the local professional observer to carry out observations at specific times [14]. Unfortunately, such a vagueness of the definition of measurement times may be one of the potential sources of the inconsistencies between the measurements (remote observations vs. METAR messages) and it has to be accounted for in their interpretation.
## 4 Simulation Results  
### Bird Strike Analysis  
#### 4.1.1 Bird Strike Analysis Using Eulerian Virtual Body  
The initial velocity of the bird was 77.2 m/s. The initial velocity was according to the take-off speed of a commercial airline aircraft [22]. The bird was aimed at 60% of the mid-span of the blades, as per the probability equations for a bird strike on an engine rotor [23]. The bird was positioned close to the fan blades to reduce the computation time. The deformation of blades is shown in Figure 5.  
The analysis of the bird strike shows that the bird interacted with nearly seven blades. A maximum deformation was noted in the first blade, which came in contact with the bird. As the bird disintegrated afterwards, less levels of deformation were noted in the rest of the blade.  
The von Misses stress generated on the blades of the fan against the bird strike are shown in Figure 6, and the von Mises stress generated in the fan blades due to the bird strike was 1255 MPa.  
Figure 5: Blade deformation at 0.3, 0.9, and 1.5 m sec, respectively.
## 3 The Proposed Protocol
### The Proposed Scheme
#### 3.3.4 UAV-UAV Authentication Phase  
Once the UAV and GCS have completed mutual authentication, we propose to use the key node to achieve mutual authentication and communication between UAVs. Firstly, multiple UAVs can be viewed as a cluster, then a suitable node from the cluster can be selected as the key node using the fitness function, and finally mutual authentication and communication between the UAVs can be achieved through the key node. Through the key node, we can enhance the destructiveness of the whole UAV communication network, making the communication efficiency between UAVs enhanced, also improving the security of the whole network.
# Generalized Replay Spoofing Countermeasure Based on Combining Local Subclassification Models
## 5 Experiments
### Experiment 3: Feature Relevance Weight Learning  
In this experiment, simultaneous clustering and attribute discrimination (SCAD) [15] was used to mine the hidden structure of the data, and learnt the relevant feature weights of CQCC, MFCC, and LFCC. First, the cluster number was set to 2 for SVM-based systems and 4 for GMM-based systems, in accordance with the obtained results in Experiments 1 and 2. Table 8 reports the learned feature relevance weights. As shown in Table 8, the largest weight was assigned CQCC. This result is consistent with Experiment 1 findings, which proved that CQCC is more suitable.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **MFCC** & **CQCC** & **LFCC** & **CQCC + MFCC + LFCC** \\ \hline \multirow{2}{*}{ASVspoof 2017} & EER & 8.24 & **1.42** & 7.29 & 3 \\ \cline{2-6}  & No. of clusters & 3 & 2 & 5 & 2 \\ \hline \multirow{2}{*}{ASVspoof 2019} & EER & 9.79 & **1.63** & 6.88 & 2.34 \\ \cline{2-6}  & No. of clusters & 4 & 2 & 2 & 2 \\ \hline \hline \end{tabular} Bold number indicates the lowest EER value.  
\end{table}
Table 6: Achieved EER when employing SVM as a classifier along with the estimated cluster numbers for each feature on the ASVspoof 2017 and ASVspoof 2019 benchmark datasets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **No of Clusters** & **EER\%** \\ \hline GMM with ALL & 15 & 24.96 \\ \hline \hline \end{tabular} Bold number indicates the lowest EER value.  
\end{table}
Table 5: _Cont._Next, we discarded MFCC and LFCC, and applied SCAD to the entries of CQCC to learn the relevance of each entry. The considered cluster numbers were between 2 and 16. Figure 7 shows the achieved EER for each cluster number when employing SCAD and SVM [17] on the ASVspoof 2017 version 2.0 benchmark dataset. The lowest EER, equal to 0.154, was achieved for 2 clusters. When using the GMM classifier, the lowest EER was equal to 0.302 with four clusters, as shown in Figure 8. This suggests that employing SCAD on the CQCC gave better performance because this approach handled the large dimension of CQCC feature by computing the weighted sum of the feature entrees.
## 3 Results
### Volumetric Geoelectrical Model of the Site  
The 3D geoelectric model of the study area was a set of one-dimensional models interpolated in three-dimensional space. Figure 10 shows the three-dimensional distribution of resistivity in the volume of the medium. The horizontal section of the model was made at an absolute elevation of 530 m. At this elevation, groundwater was discharged in the downstream at the right and left bank junctions.  
The overlay of the identified low-resistance zones on the relief elements of the valley of the Fossil Creek is shown in Figure 10.  
As can be seen from Figure 10, a linear zone of low resistivity was traced in the left bank junction, along which water filtration and discharge in the downstream are expected. In the right-bank junction, the linear zone of reduced resistivity was less pronounced.  
In the central part of the site, the area of low resistivity corresponded to technogenic deposits. At the same time, their resistivity varied from 30 to 150 Ohm\(\cdot\)m. It was assumed that such a range of resistivity changes was associated with different moisture content of the tailings. An extensive anomaly of low (10 Ohm\(\cdot\)m and less) resistivity in the section along the enclosing dam was associated with both water filtration and the influence of siphon water intakes. Thus, the three-dimensional geoelectric model made it possible to form a holistic view of the structure of the bypass filtration zones in the right and left bank junctions. Figure 11 shows a diagram of the location of faults and filtration zones, as well as the choice of places for injection of plugging compounds, compiled on the basis of the engineering and geophysical studies.  
Figure 10: Volumetric geoelectric model of the research site: 1—filtration along the fault in the right-bank (western) junction; 2—the same in the left-bank (eastern) junction.
# Object-Environment Fusion of Visual System for Automatic Pear Picking
## 2 Materials and Methods
### Picking Pose Estimation  
#### 2.4.1 Problem Definition  
Picking pose estimation requires the full integration of the results from object and environmental perception to obtain multiple collision-free picking poses, where a picking pose includes the picking position and orientation. Picking pose estimation involves two key components: a sampler and an evaluator. The sampler generates a set of picking candidates based on the 3D position of the object, where each candidate includes a picking pose; the evaluator is used to identify collision-free picking candidates based on the 3D space of the obstacles.  
To calculate the picking pose for the object, we require the transformation of three coordinate frames: object frame \(O\), camera frame \(C\), and gripper frame \(E\). As shown in Figure 7, the transformation relationships are as follows.  
1. \(\overset{C}{E}\)T: this represents the picking pose, which is equivalent to the position and orientation of the gripper in the camera frame.
2. \(\overset{O}{E}\)T: this represents the position and orientation of the gripper in the object frame, which is used to describe basic picking.  
Figure 6: Voxel-based representation of environment. The red and black boxes represent the object and obstacle, respectively.  
3. \(\overset{C}{\text{\bf T}}\): this represents the object pose, which is equivalent to the position and orientation of the object in the camera frame. According to kinematic analysis, the transformation relationships among these frames [33] can be defined as \[\overset{C}{\text{\bf T}}=\overset{O}{E}\text{\bf T}_{O}^{C}\text{\bf T}\] (2) where \(\overset{C}{E}\text{\bf T}_{E}^{O}\text{\bf T}_{O}^{C}\text{\bf T}\in\mathbb{R}^ {4\times 4}\) represent the homogeneous transformation matrix.
# An Energy-Aware Method for Selecting Cluster Heads in Wireless Sensor Networks
## 3 Proposed Method
### Appropriate Criteria  
In our proposed method, we define four key factors, which play vital roles in extending the lifetime. Therefore, we consider four key factors to prioritize the sensor nodes for selecting an appropriate cluster head. We express these factors as follows:  
1. Residual energy: Residual energy is the battery energy that remains in each node. Based on the primary energy definition, the energy consumption is calculated by Equations (1)-(3) in Table 1.  
\begin{table}
\begin{tabular}{l l} \hline Amplifier energy & \(\varepsilon_{amp}=100\) pi/bit/m\({}^{2}\) \\ \hline Electronics energy & \(E_{elec}=50\) nj/bit \\ \hline Communication energy parameters for the & \(\varepsilon_{mp}=0.0013\) pi/bit/m\({}^{4}\), \(\varepsilon_{efs}=10\) pi/bit/m\({}^{2}\) \\ \hline Energy for data aggregation & \(E_{DA}=5\) nj/bit/signal \\ \hline \end{tabular}
\end{table}
Table 1: Energy model used in the proposed method.  
2. Mobility: In order to decrease energy consumption, we consider mobility as an effective tool. The task of mobile nodes is to collect the data from static nodes, and the static nodes wait to receive data from the mobile nodes. It is worth mentioning that the connection between static nodes and mobile nodes occurs near each node. Consequently, static nodes save energy because the path length, the conflict between data that are being received or sent, and the overheads of sending data decrease severely.
3. Distance to the centre: This distance exemplifies the distance of each node from the centre of the network environment. We assume that the sensor nodes utilize GPS in order to determine the location.
4. Queue size: The queue size epitomizes the buffer capacity for each node. This parameter protects the data due to the huge amount of data in the receiver buffer.  
It is indispensable to know the main task of the base station in the installation stage. Thus, we can categorize the main task of the base station into five groups as follows:  
1. The base station considers the most appropriate nodes as cluster heads.
2. The base station calculates the optimum number of cluster heads.
3. The base station classifies the network nodes to reduce the total energy consumption in the data transfer phase.
4. The base station schedules transferring the data from non-cluster head nodes to the cluster heads.
5. The base station finds the most efficient path for the heads to communicate with each other and send data to the base station.
# Ultrasonic VMCF Tests on Very Large Specimens with Risk-Volume Up to 5000 mm+
###### Abstract  
The research on the size-effects in Very-High-Cycle Fatigue (VMCF) has recently drawn the attention of several scholars. The fatigue cracks in VMCF originate from the largest defect present within the loaded material volume (risk-volume) and the larger the risk-volume, the larger the probability of critical defects affecting the VMCF response (size-effect). Many models have been proposed in the literature to deal with size-effects in VMCF. However, the proposed models cannot be validated on full-scale components, since VMCF tests are typically carried out with ultrasonic fatigue testing machines. The authors have proposed a specimen geometry, the so-called Gaussian specimens, to enlarge as much as possible the risk-volume in ultrasonic VMCF tests. In this study, fully reversed tension-compression ultrasonic VMCF tests up to \(10^{9}\) cycles were carried out on AISI H13 steel Gaussian specimens with a risk-volume of 5000 mm\({}^{3}\), two times larger than the largest tested in the literature. The stress distribution and the absence of bending loads were verified with strain gages, proving that VMCF tests on risk-volumes of 5000 mm\({}^{3}\) can be reliably carried out. Ultrasonic VMCF tests were also carried out on small hourglass specimens, confirming that larger risk-volumes allow for a more reliable design against VMCF failures.  
ultrasonic fatigue tests; risk-volume; Very-High-Cycle Fatigue (VMCF); Gaussian specimen; size-effect; defect sensitivity; high-strength steel Article