esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In the seismic analysis of underground structures, the traditional integral response displacement method may misestimate far-field constraint effects because of the empirical placement of cutoff boundaries, leading to inaccuracies and a significant increase in computational costs. This study proposes a cutoff boundary distance criterion of three times the structural size, effectively eliminating boundary reflection errors. Furthermore, by introducing artificial boundary conditions that simulate the static resistance of a semi-infinite foundation and the far-field constraint effects, an improved integral response deformation method considering the far-field effect is developed. Verification based on the dynamic time history method (implemented with the Abaqus/Explicit solver) confirms that the computational error of the proposed method is essentially controlled within 5%, and compared with that of the traditional method, the number of mesh elements and nodes is reduced by 80%\(\sim\)90%. This advancement provides a computationally efficient and highly accurate solution for the seismic analysis of large-scale underground structures, making it highly valuable for practical engineering applications.  
Keywords:seismic resistance of underground structures; integral response displacement method; cutoff boundaries; artificial boundary conditions +
Footnote †: journal: Applied Sciences
# SDViT: Stacking of Distilled Vision Transformers for Hand Gesture Recognition
## 1 Introduction
Additionally, variations in hand pose and lighting conditions pose difficulties for existing models, impacting their robustness in real-world scenarios. The sensitivity of models to background complexity, especially when distinguishing hand gestures from intricate or cluttered backgrounds, is another notable limitation. Furthermore, the generalization capabilities of existing models across different hand sizes and skin tones remain as an ongoing concern. Discriminating between visually similar gestures, where subtle differences may be challenging to discern, poses a significant obstacle. Moreover, some existing models, particularly those based on complex architectures, may suffer from computational inefficiency and large model sizes, limiting their practical deployment. In order to address the research gaps, the proposed SDViT model incorporates the vision transformer (ViT) model along with a self-attention mechanism that empowers the model to grasp intricate connections among image sections, ultimately enhancing its capability to recognize these gestures effectively. To reduce the size of the ViT model, a knowledge distillation process is applied to obtain a smaller yet equally accurate distilled student ViT model. The final prediction is obtained through ensemble learning stacking, which combines the predictions of four distilled student ViT models. This approach reduces the risk of overfitting and improves prediction accuracy, resulting in better overall performance. The primary contributions of this research paper can be outlined as follows:  
* The ViT model, equipped with a self-attention mechanism, effectively captures complex relationships among image patches, enhancing its capability to handle challenges such as variations in pose, lighting, background, occlusions, and the issue of similarity between hand gestures. * Knowledge distillation is employed to distill the knowledge from a larger, more complex model into a smaller, computationally efficient version, enabling accurate and efficient hand gesture recognition with a reduced model size. * Ensemble learning, through the combination of multiple models, effectively reduces the risk of overfitting and enhances the accuracy of HGR predictions.
# Integrity Analysis for GPS-Based Navigation of UAVs in Urban Environment
###### Abstract  
The increasing use of Unmanned Aerial Vehicles (UAVs) in safety-critical missions in both civilian and military areas demands accurate and reliable navigation, where one of the key sources of navigation information is presented by Global Navigation Satellite Systems (GNSS). In challenging conditions, for example, in urban areas, the accuracy of GNSS-based navigation may degrade significantly due to user-satellite geometry and obscuration issues without being noticed by the user. Therefore, considering the essentially dynamic rate of change in this type of environment, integrity monitoring is of critical importance for understanding the level of trust we have in positioning and timing data. In this paper, the dilution of precision (DOP) coefficients under nominal and challenging conditions were investigated for the purpose of integrity monitoring in urban environments. By analyzing positioning information in a simulated urban environment using a software-based GNSS receiver, the integrity monitoring approach based on joint consideration of GNSS observables and environmental parameters has been proposed. It was shown that DOP coefficients, when considered together with a number of visible satellites and cut-off elevations specific to the urban environment carry valuable integrity information that is difficult to get using existing integrity monitoring approaches. This has allowed generating indirect integrity measures based on cut-off elevation and satellite visibility that can be used for UAV path planning and guidance in urban environments.  
GNSS-denied environment; DOP; integrity monitoring; navigation; urban air mobility; UAV 2020
## 2 Method
### Experiment Design  
In this study, we proposed the CNN-PPG SCR system to help severely dysarthric patients control software via their speech commands, with two well-known SCR systems (CNN-MFCC and ASR-based SCR systems) used for comparison. First, the training set (described in Section 2.1) was used to train these three SCR systems. The speech commands of the training set were converted to MFCC features (\(X_{i}^{MFCC}\)) through the toolkit of Kaldi [63]. Next, the \(X_{i}^{MFCC}\) and related label targets were used as the input and output of the CNN-MFCC and ASR-based SCR systems to learn the parameters; the detailed training approach is described in Section 2.3.1 and Section 2.3.2, respectively. Meanwhile, the \(X_{i}^{MFCC}\) with labeled 33 class phone data for Mandarin (Wade-Giles system) were used to train the acoustic model, which is described in Section 2.3.2. Following this,the trained acoustic model converted the MFCC features to PPG features (\(X_{i}^{PPG}\)), which were used to train the CNN model of the proposed CNN-PPG SCR system; the detailed setting of CNN-PPG is described in Section 2.2. Next, the testing set was used to test each SCR system to ensure the performance. In this study, the experiment was repeated 10 times, and the average results were used to compare the performance of each SCR system.  
In order to ensure the benefits of the proposed CNN-PPG SCR system in this study, a two-experiment setup was used to investigate performance. First, we wanted to evaluate the benefits of the PPG features in the dysarthric patients' SCR tasks, with the MFCC features used as the comparison. The \(t\)-distributed stochastic neighbor embedding (\(t\)-SNE) [66] method was used to further compare them. Note that \(t\)-SNE technology is a machine learning algorithm for dimensionality reduction, based on a nonlinear dimensionality reduction technique. In this test, we used MFCC and PPG features to extract speech features for all data, and these extracted features were input to the \(t\)-SNE software for analysis. Next, we evaluated the performance of the proposed CNN-PPG SCR system with the CNN-MFCC and ASR-based SCR systems by the recognition rate method, with the ten times validation test approach used to confirm the performance of the three systems. We searched for the best setting by changing the model setting in this task (for details of the model setting, refer to Appendix E (Table A5), Appendix F (Table A6), and Appendix G (Table A7)); the best performance would represent the performance of each SCR system.
## 4 Indoor AR Positioning Techniques
### Accumulated Error Correction with Periodical Calibration  
One of the significant challenges in high-accuracy indoor positioning involves the accumulation of errors as the user moves. When these errors exceed acceptable limits, geofences may not activate as intended, leading to malfunctions in the navigation system. Therefore, it is necessary to conduct positioning calibration after the user has moved a predefined distance. The calibration can be easily accomplished by recognizing a location reference marker. Nonetheless, the following three elements are deemed crucial for practical applications.  
Figure 3: Transformation of local coordinates into a global coordinate system for indoor AR positioning.  
**Frequency of calibration:** Empirical results from our experiments on positioning accuracy in spatial tracking with VIO (referenced in Section 6.1) indicate that the user will experience an error of approximately 0.3 m after moving 30 m at a normal walking speed of 3.6 km/h. Considering a human's shoulder width of approximately 0.6 m, a smartphone held at the center line of the body will have a horizontal distance of 0.3 m to the body's edge. Under these circumstances, a geofence designed to cover the width of a corridor may fail to activate if the positioning error exceeds 0.3 m. Consequently, we recommend calibration every 30 m, as this error margin is the maximum tolerable threshold for a smartphone-based indoor navigation system.
2. **Marker for calibration:** The location reference markers should be strategically distributed throughout the building to cover the navigation service area adequately. Each marker should be placed within walking distance of less than 30 m from the nearest one. In the context of museums, we recommend utilizing signboards and exhibits (suppose they are stationary) as location reference markers. This approach allows the smartphone to naturally and cost-effectively calibrate accumulated positioning errors while the user is engaged in viewing the exhibits.
3. **User interface guiding the calibration**: The user interface (UI) must support two fundamental functions: firstly, notifying the user of the need for calibration, and secondly, guiding the user to the reference marker. In our experimental application, we incorporated a gauge bar that signals the necessity for calibration. When the walking distance exceeds 30 m, and the gauge is depleted, an alert is triggered to remind the user to locate a marker. Simultaneously, the photo of the nearest marker is displayed to assist in calibration.  
Our experimental application implements the strategies based on the above three elements. Preliminary results from a demonstration experiment described in Section 6.3 demonstrate the effectiveness of accumulative error calibration.
## 2 Materials
### In Situ Data
#### 2.2.2 Soil Texture  
The samples (146) were collected along the same transects used for the measurements of topsoil moisture. Each measurement 'point' consisted of 16 core samples within a circle with a 15 m diameter and a depth of 25 cm. The number of 'points' measured in each plot ranged from 2 to 8 and is a function of (i) the length of the transect and (ii) the analysis of surface moisture measurements. The plots showing specific soil moisture profiles; namely, a trend or different levels of values along the transect were intentionally oversampled. The 'Lara Europe Analyses' laboratory obtained the fractions of clay, silts, sands and gravels.  
The fractions of clay, silt and sand are presented within the United States Department of Agriculture (USDA) classification system (Figure 4). This classification is divided into 12 classes of texture, 7 of which are assigned to the measurements. The samples are mostly in the silty loam class (with 77 samples), followed by loam (20), silty clay loam (16), clay loam (15), clay (10), silty clay (5) and sandy clay loam (3). With fractions between 9 and 58% for clay, between 22 and 77% for silt and between 4 and 53% for sand, the observed contents cover approximately half of the range of each component. On average, the texture is composed of 52% silt and 24% clay and sand, illustrating the dominance of the silt fraction within the study area. Moreover, 95% of samples have a sand content of less than 40%, and more than half of the points have a clay content that does not exceed 20%.
## 3 Results and Analysis
### Test 4: LED Current Control with Discharged Battery  
This fourth test considers a small 12-volt Nickel Metal Hydride battery (_CUSTOM_-334) with a capacity of 0.4 Ah and an initial state-of-charge of 10% (Figure 13). This configuration produces a fast battery voltage drop, which enables to test the control robustness to changes on the input voltage. Therefore, the same LED current references are adopted.  
Figure 12(a) shows a behavior similar to the previous cases because the system is correctly controlled. In Figure 12(b) it is observed that the battery voltage starts at 12.04 V and ends at 11.3 V, presenting a reduction of 6% in a time of 6 s. Despite this constant disturbance, the control system ensures the correct regulation of the current of the LEDs according to the reference \(i_{LEDref}\).  
Figure 12(c) shows the duty cycle, which behaves similarly to the case of the 12-volt battery, only that as the battery discharges, the duty cycle increases to compensate for the  
Figure 12: Results for the LED current control in buck mode with a 24-volt battery.  
voltage drop at the input. The working cycle at the end of the simulated time of 6 s had to increase 8.5% more than when the battery was charged in Test 1.  
Figure 13d shows that the \(i_{L}\) current follows the \(i_{L}ref\) reference, only now both have a positive slope due to the discharge of the battery. Both currents have a higher value than in the first test, reaching a final increase of 10.16%.
## 3 Method
### Regression Model
#### 3.4.2 Geographically Weighted Regression (GWR)  
To extend the OLS model for analyzing relationships between variables across different geographical locations and capturing spatial non-stationarity, a GWR model was constructed:  
\[y_{i}=\beta_{0}(u_{i},v_{i})+\sum_{k=0}^{j}\beta_{ik}(u_{i},v_{i})x_{ik}+\in_{i} \tag{5}\]  
In this equation: \((u_{i},v_{i})\) represents the spatial coordinates of the location of senior care facility i; \(\beta_{ik}(u_{i},v_{i})\) is the local regression coefficient for the kth independent variable, which varies with spatial location.  
An adaptive bi-square kernel function was used to determine the spatial weight matrix:  
\[\omega_{\bar{ij}}=\left[1-\left(\frac{d_{ij}}{d_{i(q)}}\right)^{2}\right]^{2} (d_{\bar{ij}}\leq d_{i(q)}) \tag{6}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variables** & **Definition** & **Mean** & **Standard** \\ \hline Dependent variables & & & **Deviation** \\ \hline
5 min life circle & Walkability score of 5-min Elderly care facilities life circle for older people & 4.8555 & 4.3594 \\
10 min life circle & Walkability score of 10-min Elderly care facilities life circle for older people & 7.6802 & 9.8110 \\
15 min life circle & Walkability score of 15-min Elderly care facilities life circle for older people & 21.7341 & 15.4122 \\ \hline Street level environmental variables & & & \\ \hline Road & The average proportion of road pixels to total pixels & 0.0674 & 0.0852 \\ Sidewalk & The average proportion of sidewalk pixels to total pixels & 0.0195 & 0.0259 \\ Building & The average proportion of building pixels to total pixels & 0.1041 & 0.1359 \\ Wall & The average proportion of wall pixels to total pixels & 0.0152 & 0.0270 \\ Fence & The average proportion of fence pixels to total pixels & 0.0192 & 0.0278 \\ Vegetation & The average proportion of vegetation pixels to total pixels & 0.1039 & 0.1305 \\ Terrain & The average proportion of terrain pixels to total pixels & 0.0305 & 0.0436 \\ Sky & The average proportion of sky pixels to total pixels & 0.0981 & 0.1177 \\ Person & The average proportion of person pixels to total pixels & 0.0019 & 0.0039 \\ Traffic vehicle & The average proportion of traffic vehicle pixels to total pixels & 0.0450 & 0.0707 \\ \hline Street population variable & & & \\ \hline Elderly & Population aged 65 and above & 6499.24 & 3029.149714 \\ population & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summary statistics of the dependent variables and the independent variables.  
In this equation: d\({}_{ij}\) is the Euclidean distance between sample points i and j; d\({}_{i(q)}\) is the distance threshold for the qth nearest neighbor. The optimal bandwidth was determined using the golden section search method (based on the AICc minimization criterion). The specific computational procedure followed the spatial calibration algorithm proposed by [PERSON] et al. [81].
# 3D Path Planning Algorithms in UAV-Enabled Communications Systems: A Mapping Study
## 6 Open Issues and Research Challenges
### Objective to Be Optimized  
The main objective to be optimized focus on path planning is the coverage, avoiding collision, and energy consumption, but only a few consider providing communication during the trip. Consequently, additional objectives are regarded to facilitate communication during UAV flights. The Outage Probability ensures reliable and uninterrupted communication services in UAV networks, especially in challenging and dynamic environments. Optimizing throughput is essential to maximize the data rate and network efficiency. Delay Among terminal devices minimizing the communications delay is vital to enhance the scalability in UAV-enable communication systems, and quality of service ensures the power signal to decode the information.
## 5 In-time Feedback of Monitoring Data  
The above research results were applied to tunnel construction in a large deformation section of soft rock. Through tracking the implementation of the scheme and field monitoring data, the research results were checked and improved, and in-time feedback provided for the tunnel construction.  
Section 4 of this paper showed that the settlement of the vault was 5 cm and the convergence of the sidewall was 8.5 cm. On this basis, the support outline was designed, and the reserved deformation was 10 cm, as shown in Figures 10-12. It can be seen that the construction of the initial part of tunnel (Figure 10) is guided by the construction suggestions given above. The actual support outline of the tunnel in this section is mostly consistent with the design, but the displacement of the right sidewall is too large. In order to ensure adequate thickness of the second lining (60 cm), the inner outline of second lining is not allowed to intrude into the tunnel clearance outline. Feedback on this situation was provided in time for later construction; e.g., to expand and excavate the right wall appropriately and increase the reserved deformation to 35 cm and construct the second lining a little earlier. It can be seen that the final deformation of the tunnel has been effectively controlled to meet the design and construction requirements from the construction of the middle and terminal parts of tunnel (Figures 11 and 12).  
Figure 10: (**a**) Typical cross-section 1 of initial part of tunnel; (**b**) Typical cross-section 2 of initial part of tunnel.
## 3 Crack Identification and Measurement System
### Crack Segmentation Based on U-Net Algorithm
#### 3.2.1 Architecture of U-Net  
The architecture of U-net including inputs, outputs and intermediate layers is as shown in Figure 8[14; 15; 16]. The left part is the contracting path, and the right part is the expansive path. As the input 256 \(\times\) 256 size images pass through the contracting path, high-resolution features are extracted by several convolution filters (Conv) with 3 \(\times\) 3 kernels to compress the images into a multi-channel feature map. The aims of proposing the activation function behind Conv are to add nonlinearity to the neural network to fit more nonlinear functions. The ReLU activation function is a commonly used activation function to strengthen nonlinear properties and is defined as follows:  
\[\text{ReLU}(x)=\text{max}(0,\ x), \tag{31}\]  
In order to reduce the number of parameters in the network and the risk of overfitting, a down-sampling operation is selected as an intermediate process of connecting each Conv block in the contracting path. The max-pooling operation, which is one of the methods of down-sampling, has been widely adopted and has been proven to preserve the features well. After encoding procedures, the features are delivered to the expansive path. Contrary to the left part of the architecture, the expansive path gradually upsizes the computed data shape to the original input image size to precisely restore and locate features. Simultaneously, the paralleled features map from the contracting path is directly passed to the expansive path by concatenation. In the end, the final Conv layer is designed with a size of 1 \(\times\) 1 to transform the output images into binary at the same size, storing each classification  
Figure 8: Architecture of U-net network for crack segmentation.  
into each pixel. To compress the classification results in a normalized form, the Softmax function is selected for normalization as follows:  
\[\text{Softmax}(y_{j})=\frac{e^{Z_{j}}}{\sum\limits_{i=1}^{N}e^{Z_{i}}}, \tag{32}\]  
where \(N\) is the total number of categories, \(j\) is the pending category, and \(Z\) is the delivered weight from the previous Conv layer.
# Quality Assessment of DSMs Produced from UAV Flights Georeferenced with On-Board RTK Positioning
## Materials and Methods  
### Test Organisation  
The experiment was organised as follows: eight flights were executed with a SenseFly eBee-RTK over the same test field and according to the same flight plan. Figure 1 shows, on the left-hand side, the data processing workflow for each flight. In four of the flights, the RTK corrections were sent from a local GNSS master station; in the remaining four, they were sent from a CORS Network. Block orientation was performed three times for each flight using a single set of tie points, but enforcing a different block control configuration: with GCPs only, with camera stations only, and with camera stations and one GCP. From each orientation parameter set, a point cloud was generated by dense matching and interpolated over a raster to produce a 10-cm resolution DSM. Therefore, the combination of the eight flights and three block control configurations generates 24 DSM.  
Ground reference data were provided for an accuracy check (Section 2.3); the experiment evaluation methods are illustrated in Section 2.5.  
Figure 1: Data acquisition and data processing workflow for each of the eight flights over the test field. On the left side, there are the settings for the main PhotoScan processing parameters.