esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Computational Efficiency Analysis
Consequently, the proposed method shows distinct advantages on high-performance platforms. Overall, the experimental results validate that the proposed method not only provides robust privacy protection but also exhibits high computational efficiency across different hardware platforms, especially on high-performance GPUs, where the advantage is more pronounced. This provides critical experimental evidence for the widespread adoption of the proposed method in large-scale real-world applications. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Raspberry Pi** & **Jetson** & **NVIDIA 3080 GPU** \\ \hline Multi-party Secure Computation & 24.64 & 37.73 & 43.91 \\ Homomorphic Encryption & 28.25 & 39.67 & 42.94 \\ Differential Privacy & 15.03 & 41.27 & 51.48 \\ Federated Learning & 26.79 & 43.02 & 48.39 \\ Proposed Method & 29.93 & 46.64 & 57.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: FPS experiment results.
## 4 Results and Discussion
### Optimal Solution for the Pilot Building  
In the pilot building, the optimal solution that was finally chosen was the package number 1 out of the 12 in Table 6. This is detailed in Table 7.  
Once the building has been renovated as shown in Table 7, the energy performance of the building is that shown in Table 8, where the building's energy performance was compared before and after the renovation; applying the optimal solutions to renovate the building could be seen to result in energy savings of approximately 60%.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{2}{c}{**Primary**} & \multicolumn{2}{c}{**Primary**} & \multirow{2}{*}{**LCC 30**} & \multirow{2}{*}{**Final Energy**} & \multirow{2}{*}{**CO\({}_{2}\)**} & \multirow{2}{*}{**CO\({}_{2}\)**} & \multirow{2}{*}{**Intervention**} & \multirow{2}{*}{**Savings/Year**} & \multirow{2}{*}{**Payback**} \\  & **Energy Consumption** & & & & & & & \\  & **(kWh/m\({}^{2}\).yr)** & **Savings** & & & & & & \\ \hline
1 & 36.8 & 57.2\% & 183.5 & 25.4 & 4.63 & 56.2\% & 42.6 & 4.63 & 9.19 \\ \hline
2 & 36.8 & 57.2\% & 197.9 & 24.7 & 4.77 & 54.9\% & 55.3 & 4.69 & 11.78 \\
3 & 37.0 & 57.0\% & 191.2 & 25.6 & 4.65 & 56.0\% & 50.0 & 4.61 & 10.84 \\
4 & 37.0 & 57.0\% & 193.7 & 25.0 & 4.76 & 55.0\% & 51.3 & 4.67 & 11.00 \\
5 & 37.3 & 56.6\% & 190.3 & 25.9 & 4.70 & 55.5\% & 48.5 & 4.58 & 10.59 \\
6 & 37.7 & 56.2\% & 184.4 & 25.1 & 4.95 & 53.1\% & 39.8 & 4.64 & 8.58 \\
7 & 37.9 & 55.9\% & 195.7 & 25.6 & 4.93 & 53.4\% & 51.3 & 4.58 & 11.20 \\
8 & 38.1 & 55.7\% & 189.6 & 25.7 & 4.96 & 53.1\% & 44.9 & 4.57 & 9.82 \\
9 & 38.3 & 55.5\% & 192.0 & 25.6 & 5.04 & 52.3\% & 46.3 & 4.58 & 10.12 \\
10 & 38.4 & 55.3\% & 185.6 & 26.7 & 4.88 & 53.8\% & 41.7 & 4.48 & 9.31 \\
11 & 39.2 & 54.4\% & 189.2 & 26.6 & 5.12 & 51.6\% & 42.6 & 4.46 & 9.55 \\
12 & 39.7 & 53.8\% & 197.5 & 27.0 & 5.20 & 50.8\% & 49.9 & 4.41 & 11.31 \\ \hline \hline \end{tabular}
\end{table}
Table 6: POS results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**System/Element**} & \multicolumn{1}{c}{**Renovation Measure**} & \multicolumn{1}{c}{**Thermal Characteristics**} & \multicolumn{1}{c}{**Inv. Cost**} \\ \hline \hline \multicolumn{1}{c}{Facade} & 7 & U = 0.31 W/m\({}^{2}\cdot\)K & 53.72 \(\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\textasci
## 2 Materials and Methods
### Defect Detection Using Convolutional Neural Network (CNN)  
In this subsection, the methodology of interface defect detection using convolutional neural networks is described. As was shown in previous chapter, due to the systematic amplitude variations caused by the effects of sample surface and interface curvatures, traditional ultrasonic non-destructive methods cannot provide reliable interface defect detection. The development of post-processing algorithms could slightly enhance the  
Figure 4: A-scan and C-scans of multiple reflections from the Sample No.1 interface. (**a**) A-scan with shown selected time intervals of 3 multiple interface reflections; (**b**) C-scan at t\({}_{1}\)-t\({}_{2}\) time interval; (**c**) C-scan at t\({}_{2}\)-t\({}_{3}\) time interval; (**d**) C-scan at t\({}_{3}\)-t\({}_{4}\) time interval. Dash boxes show the position of the defects.  
Figure 5: C-scans at specific time moments, displaying amplitude variations and the visibility of defects at different phases along the time axis.  
performance and reliability of the technique [16]; however, it could not fully eliminate the amplitude trend, keeping the detection and characterization of defects quite complex. In this particular case, though it was possible to identify all three defects in certain scans, this process required a labor-intensive examination of all scans at each time point. Such an approach is neither efficient nor practical. In contrast, convolutional neural networks offer a versatile solution, capable of recognizing complex image patterns and learning to distinguish between defective and non-defective areas, as well as variations caused by other factors.
# Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review
## 4 Remote Sensing-Based Proxies for DRM in Urban Areas
### Economic RS-based Proxies
#### 4.2.1 Macro, Regional, and Urban Economics
Building morphology is used as a proxy for extracting building use/land use information in urban areas [32; 34]. Characteristics of buildings such as size and shape have been used to extract morphological information per building and then to classify building use [87]. For example, an industrial building usually has a complex shape and a large size [123]. Similarly, and from a wider point of view, change in urban morphology has been used for economic recovery assessment by detecting businesses movements and types in large scales [121]. Land surface temperature was used to extract evolution of settlement locations and land use information after the earthquake in L'Aquila city, Italy [122]. This proxy is robust when used on coarse resolution data to extract large size settlement changes. However, it cannot identify any detailed land use information for those settlements. There are several RS-based proxies developed for economic vulnerability assessment, in contrast to other DRM components. For instance, building geometry and heights have been employed as proxies to assess the number of floors of buildings with economic activities. These two proxies may indicate the density of such activities, which is essential for computing the economic value of an urban area [39]. Building heights, when integrated with local construction regulations, provide reliable data to compute the number of floors. Moreover, a calculation of building density based on the number of floors is reliable [124]. However, it requires additional RS data, such as oblique imagery or nDSM data [125]. Farmland ratio to other types of land uses is strongly linked to economic activities and can be regarded as comprehensive characteristics of socio-economic systems [103]. Accordingly, it has been used as a proxy to detect economic activity, with higher scores signaling lower economic vulnerability in e.g., a flood scenario [103]. However, in large regions, which offer multiple livelihood options, the farmland ratio alone is not a reliable proxy for the identification of economic vulnerability. Moreover, a level of diversification of economic activities is an important factor in assessing the resilience [29; 126]. Hence, eliciting spatial proxies for a variety of economic in an ensemble is a promising approach as compared to computing the farmland ratio alone.
## 3 Methodology
### Transfer Learning Approaches
#### 3.3.3 EfficientNetV2B3  
EfficientNetV2B3 convolutional neural network architecture, a part of the EfficientNetV2 family, optimizes computational efficiency while achieving superior accuracy. It accepts input images with three color channels (RGB) and a resolution of 300 by 300 pixels. The network possesses considerable depth, comprising around 12.3 million parameters over many layers. EfficientNetV2B3 employs a compound scaling method to balance model size and performance by consistently adjusting depth, width, and resolution. Besides its scalability, EfficientNetV2B3 is especially beneficial for medical applications, where accuracy is crucial, but processing resources may be limited [47].
## 3 Theory
### Inverse Problem  
FWI is a non-linear optimization problem that needs an appropriate objective function to be minimized. The L2-norm of the data residuals as the objective function \(E\) is used in the presented study [28; 31]. \[E=\sum_{s=1}^{n_{s}}\sum_{r=1}^{n_{r}}\sum_{j=1}^{n_{c}}\int_{0}^{T}\Big{(}d_{ j}\bigg{(}\overrightarrow{x}_{s},\overrightarrow{x}_{r},t\bigg{)},s_{j} \bigg{(}\overrightarrow{x}_{s},\overrightarrow{x}_{r},t,m\bigg{)}\Big{)}^{2}\ dt \tag{8}\]  
where \(d_{j}\) denotes the observed data, and \(s_{j}\) is the synthetic data at receiver \(r\) at point \(\overrightarrow{x}_{r}\). \(n_{s}\) and \(n_{r}\) are the number of sources and receivers respectively. \(n_{c}\) is the number ofcomponents and \(T\) is the recording time. The PCG method [32] is implemented to minimize the objective function by iteratively updating the model parameters \(m\) along the conjugate direction \(\delta c_{n}\)  
\[\delta c_{n}=\delta m_{n}+\beta_{n}\delta c_{n-1} \tag{9}\]  
At the first iteration step (\(n=1\)), the model is updated along the steepest descent direction  
\[m_{2}=m_{1}+\mu_{1}\delta m_{1} \tag{10}\]  
The model is updated along the conjugate direction in all subsequent steps (\(n>1\))  
\[m_{n+1}=m_{n}+\mu_{n}\delta c_{n} \tag{11}\]  
where \(\delta c_{1}=\delta m_{1}\). \(\mu_{n}\) denotes the step length that is estimated by a parabolic line search method [33; 34; 35; 36]. The weighting factor beta is calculated using the Polak-Ribiere formulation:  
\[\beta_{n}^{PR}=\frac{\delta m_{n}^{T}(\delta m_{n}-\delta m_{n-1})}{\delta m_ {n-1}^{T}\delta m_{n-1}} \tag{12}\]  
\(\delta m_{n}=\frac{\partial E}{\partial m}\) denotes the gradients of material parameters that can be calculated using the adjoint state method [28; 37; 38; 28]. The model parameters can be density \(\rho\) and unrelaxed P- and S-wave initial \(\pi_{u},\mu_{u}\) for a viscoelastic medium assuming a constant a priori known quality factor \(Q\). The gradients of the misfit function for the unrelaxed moduli of a grid cell at a point \(x^{\stackrel{{\rightarrow}}{{\sim}}}\) can be calculated by a zero-lag cross-correlation of the forward propagated \(s\) and the adjoint wavefield \(s^{\ddagger}\) are approximated [28].
## 2 Background and Relevant Research
### Real-World Data
#### 2.3.1 RWD and Bias  
RWD can suffer from several types of biases including (but not limited to) systematic, sampling/representation and confounding bias.  
A representative example of systematic bias is the underestimation of physical activity if monitoring devices (such as mobile phones and smartwatches) are not used continuously. Sampling or representation bias may also be included in the data, since subgroups of patients in the underlying population are often under-represented or over-represented.  
An additional type of bias commonly encountered in observational RWD is confounding bias [20]. A confounder is a variable which affects both the intervention or treatment variable as well as the study variable and, hence, there is a distortion in the measure of association between the variables of interest due to the additional effect of the confounder. It is also important to be aware of possible unobserved confounders, which might affect the causal relationship of the intervention and study variables.  
To address these issues, several methods for bias estimation have been proposed in the literature [21]. Through bias estimation, the sources of bias can be identified and appropriate mitigation measures can be considered. These methods enable us to quantify bias in an inference model and assess the reliability of the model.
# Nonlinear Robust Adaptive Control of Universal Manipulators Based on Desired Trajectory
## 4 Experiment and Discussion
### Single Joint Tuning Using Sine Trajectory  
Firstly, the proposed robust adaptive control method was tuned by applying a sine trajectory with an amplitude of \(30^{\circ}\) to each joint to adjust control parameters \(K_{r}\), \(K_{e}\), \(K_{c}\), and \(\Lambda\).  
According to Equation (12), \(r=\dot{e}+\Lambda\epsilon\) is the main state variable in control system, so \(K_{r}\) determines the speed at which \(r\) converges to the sliding mode surface and has the most important influence on the control performance. It can be adjusted to as large as possible without causing oscillations. \(\Lambda\) determines the speed at which the system position error e converges to 0 along the sliding mode surface after reaching the sliding mode surface, which can be determined based on the actual achievable closed-loop bandwidth. Secondly, \(K_{e}\) is adjusted to further determine the speed at which the position error \(e\) converges horizontally to 0. The specific effect is shown in Figure 4, which represents a two-phase diagram of the \(e\) and \(\dot{e}\). \(K_{c}\) is chosen simply by satisfying Equation (25).  
The following takes the second joint with the most complex dynamic characteristics as an example for parameters tuning. According to the closed-loop bandwidth, \(\Lambda=40\) is selected. Figure 5 shows the results of the angular position of the second joint when a different \(K_{r}\) was selected. Figure 6 shows the results of the corresponding angular position errors.  
Figure 4: Two-phase diagram of single joint parameter tuning.  
Figure 3: The flowchart of the controller design.  
The following Table 1 records the control indices using different values of \(K_{r}\).  
It can be seen from Table 1 that with the increase in \(K_{r}\), the relevant error indices demonstrated a trend from large to small and then to larger. The speed error reflects the shaking  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(K_{r}\) & _e(deg)_ & _\(\dot{e}\)(deg/s)_ & _r(deg/s)_ \\ \hline
5 & 2.07 & 9.66 & 63.09 \\
10 & 1.28 & 6.29 & 39.63 \\
25 & 0.62 & 4.98 & 19.25 \\
35 & 0.44 & 5.87 & 14.38 \\
50 & 0.40 & 27.51 & 31.86 \\
100 & 0.88 & 50.10 & 54.62 \\
200 & 0.93 & 53.55 & 59.05 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The arithmetic mean of \(e\), \(\dot{e}\), and \(r\) using different values of \(K_{r}\).  
Figure 5: (a) Joint angular position when \(K_{r}\) = 5, 10, 25, and 35; (b) joint angular position when \(K_{r}\) = 35, 50, 100, and 200.  
Figure 6: (a) Joint angular position errors when \(K_{r}\) = 5, 10, 25, and 35; (b) joint angular position errors when \(K_{r}\) = 35, 50, 100, and 200.  
in the motion. Therefore, after weighing the two items of \(e\) and \(\dot{e}\), \(K_{r}=35\) was selected as the final control parameter, and \(K_{e}=150\) was selected accordingly. In Figures 5 and 6, it can be found that the tracking performance of the red curve corresponding to \(K_{r}=35\) had a significant advantage over other curves.  
The selection of parameters in the experiment is given in Table 2.
# Stress Estimation Using the Acoustoelastic Effect of Surface Waves in Weak Anisotropic Materials
## 4 Specimens and Experiments  
### Specimens and Measurement of the Anisotropy Rate  
To verify the proposed method experimentally, stress estimation experiments were performed. Two kinds of Al6061 material with different acoustic anisotropy rates were used as the test specimens. One (SA) was produced by a hot rolling process and the other (SB) was produced by an extrusion process. Two test specimens were prepared for each material as shown in Figure 6. One specimen was used to check the anisotropy and acoustoelastic coefficient, and the other was used to verify the stress estimation method. The size of the hot rolled specimen is 350 mm \(\times\) 62 mm \(\times\) 4 mm (length \(\times\) width \(\times\) thickness) and the size of the extruded specimen is 350 mm \(\times\) 60 mm \(\times\) 5 mm.  
To verify the surface wave velocity distribution and anisotropy rate of each specimen, the velocity was measured in 24 directions with an interval of 15\({}^{\circ}\). The experimental setup is shown in Figure 7. PZT transducers with a center frequency of 2.25 MHz and wedges (ABWML-7T-90, Olympus) were used for surface wave excitation and reception. A pulser-receiver (Olympus 5077 PR) was used for the excitation with a single pulse and a high-resolution digital oscilloscope (Lecroy HDO4034A) with a 10 GS/s sampling rate was used for precise measurement of small velocity changes. The surface wave propagation distance was fixed at 10 mm using a jig. The time of flight (TOF) was used to measure the change rate of the surface wave velocities or surface wave velocity ratios used in Equations (11)-(15). The TOF was measured based on the arrival time of the peak of the pulse signal. In addition, the delay time irrespective of surface wave propagation on the specimens, such as the delay in the wedge, was compensated by calibration in advance.  
Figure 6: Aluminum plate specimens used in experiments.  
Figure 7: Surface wave velocity measurement set-up, in which the distance fixing zig holds the wedges to keep the surface wave propagation distance constant.  
The measurement results of the surface wave velocity distribution are shown in Figure 8, in which the velocity was normalized with respect to the maximum velocity. Maximum velocity (\(V_{max}\)) and minimum velocity (\(V_{min}\)) in the hot-rolled specimen are 2967.1 m/s and 2957.5 m/s, respectively, and they are 2951.8 m/s and 2876.2 m/s, respectively, in extruded specimen. Figure 8a display the results of the hot-rolled aluminum specimen (SA.1) and Figure 8b shows the results of the extruded aluminum specimen (SB.1). The acoustic anisotropy rate of the hot-rolled aluminum specimen (SA.1) was 0.005 (0.5%) and that of the extruded aluminum specimen was 0.026 (2.6%). This demonstrates that specimen SA is nearly isotropic while specimen SB has a weak anisotropic feature.
# A Study of High-Efficiency Laser Headlight Design Using Gradient-Index Lens and Liquid Lens
## 4 Conclusions  
Diode lasers demonstrate outstanding performance in automotive lighting applications because of their excellent characteristics. The optical system application that was devised as an automotive lighting solution was verified in this study using a simulation. This proposed research method is simply a simulation of the system, not actually implemented for reasons related to costs of the experimentation. The following conclusions can be drawn:  
1. Optical fibers exhibit an excellent performance in terms of laser beam transmission. Along with the capability to transmit light, ROD can adjust the polarization of laser light to achieve precise light pattern control. Using the optical fiber application, the laser sources can be freely configured outside the optical system, which is helpful when dissipating the heat of the laser source.
2. Optical fibers and RODs, which are capable of effectively and accurately adjusting the laser light pattern, can homogenize the laser light through total reflection and eliminate laser light polarization, thereby influencing the light pattern adjustment.
3. The functions of a liquid lens can be used to easily satisfy the demand for switching between the high beam and low beam. If the light pattern is controlled by the array module, it can meet the functional requirements related to the lighting of a specific area.
4. The use of freeform lenses to adjust the light pattern provides the perfect balance between the aesthetic requirements of a vehicle and its functional lighting requirements. In addition, the beam pattern of the diffused laser can satisfy the regulatory requirements for aesthetic and practical purposes after performing adjustments with a freeform lens.  
The headlight optical system designed in this study satisfied the regulatory requirements, both in terms of uniformity and illumination. The maximum illuminance is 56.6 lux in the high-beam mode, which is 18% higher than the standard value (48 lux).  
In the low-beam mode, the illuminance meets regulatory standards. Compared with a 24 W LED headlight module, this design saves 33% more energy. This power of this design is 16 W.  
If combined with opto-mechanic control and integration, this system could increase or decrease the illumination in a specific area, thus providing a feasible solution for headlight optical systems.  
Y.-C.F. and C.-H.C. for concept of optical design. Y.-L.S. and H.-Y.L. for Light Tool optical software work. Y.-F.T. for optimization work. C.-C.W., and S.-H.C. for road safety investigation. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Physical and Respiratory Rehabilitation in Spinal Muscular Atrophy: A Critical Narrative Review
## 3 Results
### Respiratory Interventions  
#### 3.2.1 Niv vs. Tracheostomy/Supportive Care  
[PERSON] et al., in the 2000 study, aimed to investigate whether type 1 SMA could be managed without tracheostomy and whether extubation results could be compared using a protocol for respiratory muscle support [44]. Eleven type 1 SMA children were studied during episodes of respiratory failure, and nine of them required multiple intubations. In addition to standard treatments for nutrition and hydration, these children underwent a protocol with manual and mechanically assisted coughing to reverse the decrease in oxygen saturation associated with airway mucus. The pressures used ranged from 25-40 cm H2O to 225-240 cm H2O for up to 10 min, depending on the needs. Abdominal thrusts were applied during expiration. Extubation was not attempted until, primarily, there was no longer a need for oxygen to maintain oxygen saturation above 94%. After extubation, all patients received nasal positive end-expiratory pressure ventilation.  
[PERSON] et al., in the 2002 study, recruited 56 patients with SMA1 who developed respiratory failure before the age of 2 and divided them into three groups based on treatment [43]. The 33 patients in Group B were initially prescribed oximetry, mechanical insufflation-exustillation (MI-E), and high-flow PIP + PEEP, which was used during sleep and upper respiratory tract infections, with intervals of 13-17 cm H2O and positive inspiratory pressures in the airways up to 20 cm H2O. Positive pressure was applied at 35-40 cm H2O during upper respiratory tract infections to expel airway mucus. For non-cooperative infants, nasal interface placement and initiation of PIP + PEEP during sleep were employed.  
[PERSON] et al. investigated if the initiation of non-invasive mechanical respiratory interventions in patients with SMA I was associated with longer survival compared to those who received only supportive care [50]. Proactive management of chronic respiration included airway clearance through mobilisation of secretions with manual or mechanical chest physiotherapy and cough assistance, as well as BiPAP ventilatory support during sleep. Supportive care was defined as other respiratory support, such as supplemental oxygen and suctioning.