esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Statistical Analysis  
The normal distribution of the data collected was assessed based on the Shapiro-Wilks statistic. The measured insertion torque, implant stability quotient at the moment of implant insertion (ISQ0), and implant stability after three months (ISQ3) failed to follow a normal distribution, so non-parametric statistics were used. In the statistical description of the sample, the mean and standard deviation were used for describing the objectively measured bone density, and the median and interquartile range were used for describing the insertion torque. The implant stability quotients (at implant placement and after three months) were the quantitative variables. The absolute frequency and percentage in categories were used for describing the nominal variables. Although some of the implants were placed in the same patients, all were considered independent because they were objectively or subjectively individually characterized during data collection. The collected data were considered paired when comparing the implant stability quotient measured at the time of implant insertion and after three months. Depending on the normal distribution  
Figure 2: Implant placement and measurement of implant stability quotient (ISQ).  
assessment, [PERSON]'s or [PERSON]'s correlation coefficients and parametric tests (Student's _t_-test) or non-parametric tests (Mann-Whitney (M-W) test) were used to compare the effects on the studied variables (insertion torque, ISQ0, and ISQ3) of the implant-related factors (implant macro geometry (cylindrical or conical), implant location (incisor-canine, bicuspids-molars), dental arch (mandible or maxilla), implant dimensions (diameter and length), intraoperative variables (bone density and bone quality determined based on the clinician's tactile sensation), and patient-related factors (gender (male, female) and age (>55, <55)). Then, significant predictors for the ISQ0 and ISQ3 were identified through a stepwise linear regression analysis. Finally, a stepwise linear regression analysis was performed using ISQ0 and ISQ3 as the dependent variables and the significant predictors previously identified. SPSS 27 statistical software (SPSS Statistics, version 27, IBM Corp) was used. The significance was set at _p_-values of \(\leq\)0.05.
## 2 Natural Ventilation-Enabling Facade Noise Control Devices
### Balconies
They observed that the balcony insertion loss showed an increasing trend with increasing balcony depth. However, the effect of floor height on the insertion loss did not show a well-defined trend, most probably because of the \"incident angle sensitive\" reflections; the ceiling inclination angle also resulted in no trend being indentified. The balcony insertion loss was found to vary between 0.5 to 6 dB. However, the maximum insertion loss found was 2, 4, and 6 dB, for a balcony depth of 1 m, 2 m, and 3 m, respectively, when the front parquet remained vertical [49]. It was also found that an inclined front parquet could result in an additional 0.5 to 4 dB noise attenuation [50]. However, a 3 m deep balcony is by no means common in a congested high-rise city. [PERSON] et al. [51] illustrated the combined effects of the ceiling inclination, sound absorption, and front parquet design, on reducing the noise inside a high-rise building balcony, using a 1:50 sixteen-storey scale model building and simulations. They used 3 mm thick dense polystyrene as the sound absorption material. They found that the noise reduction could be as high as 23 dB. It should be noted that the noise reduction defined by [PERSON] et al. [51] was the difference in the noise level inside the balcony, with and with the abovementioned special treatments. However, there was only one measurement on the rear wall of the balcony, and thus, the uncertainty in their study could be high. [PERSON] et al. [52] summarized an experience of improving the balcony insertion loss by using sound absorption (NRC 0.60) and a small inclined screen at the balcony edge. A real 40-storey building next to a busy traffic trunk road was chosen for their study. The measured insertion losses with sound absorption installed on the ceiling and side-walls, were within 4 to 5 dB. In general, sound absorption is not preferred by the users, due to the difficulty in maintaining it. [PERSON] and [PERSON] [53] proposed the use of specially designed hard surface reflectors on the balcony ceiling, in order to reflect incident noise back into open space. The amount of transmitted acoustical energy was thus reduced. By using a site measurement, they showed that the reflectors, when properly oriented, could provide an additional noise reduction of 7 to 10 dB, when compared to that which can be achieved by a conventional balcony. However, the reflectors have to be carefully designed and positioned. A large ceiling height is also required. It can be concluded that balconies on a high-rise building do not offer much acoustical protection, unless sound absorption and/or proper ceiling reflectors can be installed. However, reverberation within the street canyons of a high-rise city will largely erase the acoustical protection of the balconies. [PERSON] et al. [54] further illustrated that there would be a drop in speech intelligibility inside the balcony of a high-rise building located within a street canyon, after the installation of sound absorption.
###### Abstract  
The analysis of the degree of surface deformation can be a relevant aspect in the study of surface stability conditions, as it provides added value in the construction of risk management plans. This analysis provides the opportunity to establish the behaviors of the internal dynamics of the earth and its effects on the surface as a prediction tool for possible future effects. To this end, this study was approached through the analysis of Synthetic Aperture Radar (SAR) images using the Differential Interferometry (DInSAR) technique, which, in turn, is supported by the Small Baseline Subset (SBAS) technique to take advantage of the orbital separation of the Sentinel-1 satellite images in ascending and descending trajectory between the years 2014 and 2021. As a result, a time series was obtained in which there is a maximum uplift of 117.5 mm (LOS-ascending) or 49.3 mm (LOS-descending) and a maximum subsidence of \(-\)86.2 mm (LOS-ascending) or \(-\)71.5 mm (LOS-descending), with an oscillating behavior. These deformation conditions are largely associated with the kinematics of the Bucaramanga Fault, but a recurrent action of deep seismic activity from the Bucaramanga Seismic Nest was also observed, generating a surface deformation of \(\pm\)20 mm for the period evaluated. These deformations have a certain degree of impact on the generation of mass movements, evaluated by the correlation with the LOS-descending images. However, their action is more focused as an inherent factor of great weight, which makes it possible to respond to early care and allows real-time follow-up, giving positive feedback to the system.  
surface deformation; tectonic environments; seismic events; mass movements; trigger Article
## 4 Implementing Models with the Geographic Automata Add-In: Two Case Studies
### Case Study 2: Comparing ML-CA Models of Urban Growth
#### 4.2.3 Results  
The _Basic CA_ model execution time was 12, 10, 10, and 30 s for each model type, respectively. The simulated outcomes for years 2010 and 2020 produced by each model configuration are shown in Figure 13, and corresponding metrics obtained from the _Model Evaluation_ tool are presented in Table 6.  
In both calibration and validation metrics (Table 6), there is a general upward trend observed from the LR-CA to AutoML-CA\({}_{2}\) models. The LR-CA models are associated with the lowest overall accuracy, Kappa, and FOM values for both calibration and validation stages, which adheres to comparative findings reported in other studies [38; 50]. This model  
\begin{table}
\begin{tabular}{c l c c c c} \hline \hline  & **Metric** & **LR-CA** & **RF-CA** & **AutoML-CA\({}_{1}\)** & **AutoML-CA\({}_{2}\)** \\ \hline \multirow{2}{*}{Calibration 2000–2010} & Overall Accuracy (\%) & 81.31 & 86.64 & 86.80 & 89.41 \\  & Kappa & 0.61 & 0.71 & 0.72 & 0.77 \\  & FOM & 0.24 & 0.33 & 0.34 & 0.35 \\ \hline \multirow{2}{*}{Validation 2010–2020} & Overall Accuracy (\%) & 75.72 & 85.58 & 86.05 & 87.66 \\  & Kappa & 0.52 & 0.71 & 0.71 & 0.74 \\ \cline{1-1}  & FOM & 0.32 & 0.44 & 0.45 & 0.46 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The CA model calibration and validation metrics calculated for the four model types using the _Model Evaluation_ tool.  
Figure 13: Real urban area maps versus simulated outcomes for years 2010 (calibration) and 2020 (validation) for each model type.  
also produced the most \"false alarms\", meaning that there were more new developments forecasted to actual undeveloped locations than the other model types (Figure 14). Measures of agreement and error associated with RF-CA and AutoML-CA\({}_{1}\) model outcomes are most similar. Meanwhile, the overall accuracy and Kappa measures related to AutoML-CA\({}_{2}\) suggest the quantity limit imposed in Rule D (Table 5) helped to reduce false alarms, which is confirmed in the _Change Error Assessment_ measures graphed in Figure 14. Overall, this abridged ML-CA model comparison showed how real end-to-end model-building procedures can be conducted using a combination of the _Geographic Automata_ add-in and ArcGIS Pro functionality. Once the stages of CA model calibration and validation are completed, the best performing ML-CA model can be used to project urban growth for more iterations to obtain simulated maps of years 2030, 2040, and beyond. In addition, the model can be adapted to explore possible scenarios such as simulating faster or slower urban expansion based on population projections or examining the effects of new forest conservation or urban densification policies for the municipality.
# Discrete Environment-Driven GPU-Based Ray Launching: Validation and Applications
## 2 The DED-RL Algorithm
### Further Extensions to the DED-RL Algorithm  
With respect to the algorithm presented in [21], the DED-RL features have been further extended in order to model:  
1. efficient Over-Rooftop (ORT) propagation through multiple diffractions over building rooftops;
2. attenuation through vegetation;
3. transmission through buildings at a limited extent, considering only the closest building to the Base Station (BS).  
Regarding feature 1, it is well known that ORT diffraction is one of the dominant propagation mechanisms for the coverage of Non-Line-of-Sight (NLOS) locations when the BS is placed near or above the average rooftop level. In DED-RL, ORT diffraction is computed with a simplified approach using a multiple-screen UTD model limited to the vertical plane [2; 4; 5], considering one/two knife-edges for each building along the radial line between the BS and the receiving tiles. The ORT computation is performed for all the tiles that are not directly visible from the BS: since each ORT path is independent from the others, this computation is highly parallelizable, and is then implemented very efficiently on the GPU. Moreover, ORT rays are combined with reflection and scattering in a straightforward way, by using the visibility matrix: in fact, once a tile is reached from an ORT ray, the coverage can be extended through reflection/scattering to all the surrounding tiles that are directly visible from it.  
Figure 2: Example of 3D radio coverage prediction in the San Francisco environment.  
Regarding feature 2, vegetated areas are modelled in a simplified way as \"polygons\" with a certain height, and then a ray-polygon intersection check is carried out every time a ray tube is launched from the BS, or a tile: the additional attenuation of the ray is then computed through multiplication of the ray length falling inside each polygon by a specific attenuation coefficient, expressed in dB/m [30].  
Regarding feature 3, this mechanism can be very important for coverage of locations behind the Tx antenna, especially for microcellular sites when the BS is placed close to a corner of a building. In order to model transmission through the closest building from the BS, such a building is temporarily made \"transparent\" when finding the visible tiles from the BS: in such a way, those tiles that are obstructed by the building become automatically visible. After that, RL computation continues in the usual way with field computation and bouncing from all tiles, including the ones hidden by the building. Finally, only for the rays involving hidden tiles, an additional through-building attenuation is computed by the simplified formulas introduced in [31; 32].  
Figure 3 shows a simplified scheme describing the macrostructure of the model, including ORT and attenuation from vegetation, and the overall computation flow. A more detailed flow-chart of the algorithm can be found in [21].
## 8 Energy Optimization Techniques in UAV-Based Cellular Networks
### Energy Optimization of Standalone UAV Deployments
#### 8.1.1 Positioning and Placement  
Here, we review the conventional and ML algorithms used for optimal positioning or placement of the UAV-BS in order to minimize their energy consumption. The positioning includes optimizing the location, altitude, and radius of coverage of the UAV-BS to maximize EE while ensuring QoS of the ground users. We consider the positioning of single/multiple UAV-BS/BSs.
# AI-Driven Safety and Security for UAVs: From Machine Learning to Large Language Models
## 3 AI for UAV Safety  
Ensuring UAV safety is a fundamental requirement for their effective operation in complex environments. UAV safety includes a wide range of technologies to protect UAV systems from attacks, enhance UAV anti-interference capabilities, durability, and reliability, and ensure that UAVs can execute tasks safely and reliably. However, the large number of UAVs, their wide distribution, flexible networking methods, and complex parameters significantly increase the difficulty of ensuring their safety and protection.  
The key safety technologies include physical safety design, battery safety management, sensor spoofing detection, collision avoidance, path planning, and loss of flight control protection. Physical safety design, which includes optimizing UAV impact resistance and structural stability through the development of high-performance materials, suppressing flutter, and predicting structural stress, is the basis of UAV safety. Battery safety management involves monitoring the condition of batteries, predicting their remaining power and health status, and combining fault diagnosis and safety protection measures to ensure reliability and safety. By securing the physical design and battery, defenses against sensor spoofing can prevent UAVs from deviating from their flight paths or crashing due to false information. When sensors operate normally, collision avoidance and path planning can be conducted to combine real-time obstacle detection with optimization algorithms to achieve dynamic obstacle avoidance and safe trajectory planning. Finally, loss of flight control protection aims to enhance the autonomous navigation capabilities of UAVs to prevent loss of control due to navigation or electronic fence failures. Together, these technologies facilitate the creation of a layered and comprehensive safety protection framework, ensuring the secure flight operations of UAVs and establishing a strong foundation for their extensive adoption.
## 5 Field Application
### Monitoring Data  
Typical monitoring curves obtained by some of these sensors during working face propulsion are shown in Figures 16-18. The NPR stress monitoring curve in Figure 16 shows that the force on the constant-resistance large-deformation anchor cable increased several times in the course of advance along the working face. On the basis of the installation position of the sensor, the timing of stress changes and the position of the working face at those times, force change on the anchor cable can be attributed to the initial pressure of the roof and the periodic pressure of the working face. When the working face has been pushed forward a certain distance, the force in anchor cable becomes basically stable.  
The separation monitoring curve in Figure 17 shows that both the deep base point--which is fixed in the stable rock stratum at 5 m deep of the borehole--and the shallow base point--which is fixed at 2-2.5 m of the drill hole--tend to give constant separation values after a slow rise. The separation value of the shallow base point is about 14 mm and that of the deep base point is about 33 mm. When the roof is fitted with a dense, constant resistance 10.5-m-long anchor rope after the formation of the  
Figure 16: Force monitoring curve of the constant-resistance large-deformation anchor cable 10 m from the open-off cut.  
Figure 15: Schematic map of station layout.  
roadway and considerable pre-tightening force is applied, the roof experiences a certain degree of elastic compression. As time goes on, this elastic compression is bound to rebound in part or altogether. Therefore, the data measured by the separation sensor is not the actual roof separation and there may be springback deformation of the roof.  
The monitoring curve of roadway roof and floor proximity in Figure 18 shows that it increases gradually in the roadway area after the working face has been pushed forward. When the working face is pushed forward a certain distance, the monitoring curve tends to level out, indicating that the rock surrounding the roadway becomes basically stable at this time, with no significant increase in proximity between the roof and floor of the roadway.  
The monitoring curves shown in Figures 16-18 can be displayed in the terminal display system to reflect activity in the rock surrounding the roadway at the construction site in real time. The monitoring system will automatically sound an alarm when the stress monitoring curve of the anchor cable begins to increase suddenly, and the increment exceeds 350 kN. It indicates that the roadway will be in an unstable state. At this time, the supporting form or strength of the roadway can be optimized and adjusted immediately, or emergency measures can be taken to evacuate workers from the working face to ensure the safety of mining, roadway formation and roadway retention.
# Highly Adaptive Reconfigurable Receiver Front-End for 5G and Satellite Applications
## 5 HARRF LNA Design
### K/Ka-Band LNA Design Procedure
#### 5.2.2 K/Ka-Band LNA S-Parameter Analysis  
The LNA S-parameters are shown in Figure 14. It can be observed that the output and input return losses are greater than 10 dB for the entire band. The gain (Figure 15) was 30 dB with a ripple of 1 dB across the required band while the minimum noise (Figure 16) at the resonant frequency of 25 GHz was 1.5 dB.
# A Flexible Localization Method of Multi-Constrained Free-Form Surface Based on the Profile Curves
## 3 The Localization Problem of the Near-Net Shape Blade Surface
### Implementation of Optimal Localization Algorithm  
The blade localization before the finish machining is essentially an optimization. The input of this optimization is a set of profile curves \(c_{i}(v)\) of the nominal surface, the measured points \(p_{i}\) of the blade billet, and the segmented profile tolerance requirements. The output of this optimization is the position of the optimized profile curves.  
The profile curves of the to-be-cut blade can be obtained as follows:  
Step 1: A B-spline surface \(\mathbf{S}(u,v)\) is constructed through a set of profile curves \(\mathbf{C}_{i}(v)\). The initial translation value is \(\mathbf{T}^{(0)}\) and the initial rotation value is \(\mathbf{R}^{(0)}\).  
Step 2: According to the affine deformation of the B-spline curves and surfaces, the new profile curves \(\mathbf{C}_{i}^{{}^{\prime}}(v)\) are obtained by the space transformation of the control points of the curve \(\mathbf{C}_{i}(v)\) and obtaining the new lofting \(\mathbf{S}^{\prime}(u,v)\) according to the \(\mathbf{C}_{i}^{{}^{\prime}}(v)\).  
Step 3: By the Newton iterative method, the closest points \(p_{i}^{\prime}\) on surface \(\mathbf{S}^{\prime}(u,v)\) are obtained, and the least squares distance from the measuring points \(p_{i}\) to the closest point \(p_{i}^{\prime}\) is calculated.  
Step 4: According to the material allowance constraint, the constraint condition \(h3\) is obtained. Moreover, the constraint equations \(h1\) and \(h2\) are obtained according to the profile curves within the profile tolerance band.  
Step 5: According to the objective function and the constraint equations \(h1\), \(h2\), and \(h3\), the optimization variables are updated during the optimization.  
Step 6: It is judged whether the calculation results satisfy the constraint and satisfy the \(\|x^{(k)}-x^{(k-1)}\|\leq\varepsilon\); if it is satisfied, the iterative process converges to an optimal solution.  
Step 7: If the conditions of step 6 are not met, then according to the updated \(x^{(k)}\) the process returns directly to step 2. If the number of iterations \(k\leq N\) is satisfied, then the optimization solution \(x^{(k)}\) is obtained. Otherwise, there is no solution to the optimization.
# A Single Simulation Platform for Hybrid Photoacoustic and RF-Acoustic Computed Tomography
## 4 Discussion
### Simulation Workflow for both PACT and RACT  
We first tested our algorithms on a simple phantom that consisted of two absorbing regions of different shapes inside a homogeneous background. The second phantom simulated a human breast with a tumor. The same phantom mesh was used to define both optical and RF-acoustic properties of the tissue. The optical diffusion equation was solved to obtain the optical fluence maps of the phantoms (Figures 2 and 6), while the solution to Maxwell's equations mapped the electric field distribution for these phantoms (Figures 4 and 8). Subsequently, maps of the initial pressure rises, due to electromagnetic absorption and the thermoacoustic effect, were generated using the optical fluence maps in PACT and electric field intensity in RACT. The initial pressure distributions of both PACT and RACT were then propagated using the same time-domain equations. Respective maps of the reconstructed pressure were generated using a time-reversal algorithm. Optical absorption and conductivity of the tissue phantoms were recovered by dividing the related reconstruction pressures by the optical fluence and electric field intensity distributions, respectively (Figures 3, 5, 7 and 9).
# Research on the Correlation Filter Tracking Model Based on the Deep-Prunned Feature Network
## 5 Discussion  
To analyze the effectiveness of the pruning algorithm, the alternative convolution kernel, and the online updating algorithm more intuitively, the above experimental results are integrated as shown in Table 5.  
According to the results shown in the table, the tracking speed of the feature network without pruning operation is 68.5 FPS, while that of the feature network with deep pruning is 99.5 FPS, which is a 45% increase in speed. In terms of the tracking accuracy, the APE reaches 0.70 when alternative kernels are introduced into the feature network, which is the same level as the feature network before pruning. After further introducing the feature network online updating algorithm, the APE reaches 0.73, which is 4% higher than that of the deep-pruned feature network. Based on the above experimental results, the feature network pruning algorithm can simplify the network structure and effectively improve the tracking speed. The online updating algorithm can improve the adaptability of the feature network and increase the tracking accuracy when the tracking scene changes.