esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Pressure Sensor Concept and Design
### Membrane Sensor (MS)  
#### 3.2.1 Geometry and Design  
The concept of the second configuration consists in creating an element that can deflect under a certain amount of pressure while remaining in the elastic domain. Therefore, a membrane-like cylindrical sensor was built, which acts as a flexible element once the pressure is applied on its upper surface (Figure 4a). The sensor has the same dimensions of the water collecting cap (approximately 35 mm in diameter and 14 mm in height). The deflection of the sensor is emphasized by the difference between its inner and outer thickness. A strain gauge is glued on the sensor back face aiming to measure its deformation once pressurized. To have a maximum gauge/wires protection from fluid leakage, a sealing joint grove was shaped at the sensor outer diameter. Figure 4b shows the membrane sensor once placed inside the water collecting cap. It is worth noting that the MS sensor has two advantages over the HS one: first, the MS sensor guarantees a good sealing of the gauge/wires from the confining fluid and, second, the MS sensor is more sensitive and thus tends to amplify the output signal during measurements.  
Figure 3: Aluminum sensor of hydrostatic type: (**a**) cross section of the collecting cap with the hydrostatic sensor dimension; and (**b**) picture of the cap and pressure sensor.  
In this study, it was supposed that the membrane sensor could measure a pressure as high as 400 MPa. The thickness of the sensor was optimized by a series of numerical simulations, as shown in Figure 4c and described in Table 1. The total length \(l_{total}\) and the diameter \(d\) of the sensors were set equal to 14 mm and 35 mm, while the thickness was varied among 2.5, 4 and 7 mm. The inferior length \(l_{inf}\) of the three sensors was equal to 4.5 mm, which is considered sufficient for the gauge installation. The ideal sensor is the one which has the highest elastic deformation under pressure.
# Energy-Efficient Amplifiers Based on Quasi-Floating Gate Techniques
## 3 Energy-Efficient Amplifiers Based on QFG Techniques
### Multistage Class A/AB Amplifiers  
When both high gain and high output swing are required, usually multistage amplifier topologies are employed. For instance, the conventional Miller amplifier can provide class AB output by employing the circuit of Figure 4c at the output stage [25]. The resulting circuit is shown in Figure 7a, yielding output currents much larger than the bias current \(I_{B}\) and increasing GBW, due to the extra transconductance provided by M\({}_{8}\). It is denoted as a class A/AB amplifier, since the input stage operates in class A and the output stage in class AB. Other examples of class A/AB amplifiers can be found in [26; 27].
# Micro-Nanobubble Technology in Surface Cleaning and Defouling
## 5 Summary and Future Perspectives  
MNB technology is a revolutionary invention with a wide range of applications in sectors such as environmental remediation, water treatment, agriculture, and healthcare. Together, the investigations discussed in this paper highlight the special chemical and physical characteristics of NBs, such as their stability, high surface area-to-volume ratio,  
Figure 17: SEM images of **(a)** the fouled membrane surface and **(b)** cleaned membrane surfaces and the cross-sectional area of **(c)** the fouled membrane and **(d)** cleaned membrane. **(e)** Proposed mechanisms of the ceramic membrane cleaning process. Reproduced with permission from [110], CC-BY-NC-ND 4.0 license, Copyright © 2024, the authors. Published by the American Chemical Society. and capacity to produce ROS. These characteristics allow them to outperform traditional approaches in advanced cleaning, fouling mitigation, and biofilm control while offering economical and environmentally friendly substitutes (Table 1). Understanding of the mechanics behind the behavior of NBs, such as their interactions with surfaces, the function of the zeta potential, and cavitation dynamics, has advanced significantly. As a result of these discoveries, applications ranging from improved water treatment systems to precise surface cleaning have advanced. For instance, it has been shown that adding NBs to membrane cleaning procedures significantly lowers fouling and increases operating effectiveness. Their use in biofilm removal in wastewater treatment and disease control in the food industry further demonstrates their adaptability and promise for solving urgent environmental issues. Although MNB technology--which encompasses both MBs and NBs--offers tremendous potential for several industries, its effective application necessitates overcoming a number of transdisciplinary obstacles. One of the main challenges is bringing MNB production techniques like membrane injection and ultrasonication from lab to industrial scales. Making the technique feasible for large-scale applications requires optimizing energy consumption while achieving consistent bubble size and production rates. To guarantee the effectiveness and viability of the technology, certain engineering challenges must be overcome. Although MNB technology exhibits potential in soil remediation and water treatment, its long-term impacts on ecosystems are not entirely known. It may be possible to change microbial populations or have an impact on local biodiversity by introducing high-surface-area gas bubbles into natural settings. Aquatic life may be unexpectedly impacted by ozone-enriched NBs in aquatic systems, for instance. To make sure that MNBs
## 4 Discussion
### Possible Future Improvements  
The feature selection for the RF classification was performed by identifying the potentially most-relevant textural indices and iteratively analyzing the feature importance after the classification. In similar LULC classification studies, a dimensionality reduction in textural indices was successfully performed [21; 93]. However, the attempts to adopt a feature reduction step generated a block in the GEE code execution, probably due to the study area's size. In this regard, additional experiments are required to test these approaches in OB LULC classifications based on multi-sensor datasets.  
As highlighted in previous studies [16; 21], all object recognition and subsequent characterization steps were developed in the raster domain, where GEE excels. However, due to the size of the study area, various export-to-asset steps were necessary to overcome the GEE image size limitations. At the same time, the selection of the most relevant spectral bands and indices, comparing the features' importance in the RF classifications, was a significant step in reducing the classification datasets' size. However, the proposed approach excludes consideration of object size and shape, which may be critical for objects' classification at higher resolutions [94]. The vectorization of objects may overcome this limitation. Nonetheless, in the raster-oriented GEE environment, the few attempts we performed in this direction resulted in prolonged code execution or a processing block due to the high number of geometries.  
As in similar studies, the SNIC algorithm efficiently delineated the landscape patches to be classified. Its application is usually based on a uniformly spaced grid of seeds and does not consider the local variability of objects' size. Some attempts performed in this research to use seeds created by detecting the local minimum or maximum of variance, as suggested in previous research [16], did not produce better results. In this regard, additional research to test and develop new seeding methods will be relevant.  
In addition to the thematic accuracies, it could be interesting to quantify the geometric accuracies of classifications, particularly relevant for the OBIA methods, to highlight the efficacy of the object delineation [95]. However, in addition to being particularly demanding due to the complex data acquisition, these assessment results are more suitable for OBIA on very-high-resolution data, where the landscape objects are more specifically identified [96].
# Optimized Wireless Sensor Network Architecture for AI-Based Wildfire Detection in Remote Areas
## 4 Experimental Results
### Computational Complexity Analysis
#### 4.3.3 Topology Deployment and Energy Efficiency  
Node placement calculations (Equations (2) and (3)) are executed in \(O(1)\) per tier using closed-form geometric formulas. The star/circle topology reduces node count by 53-55% versus 2D Mesh (Equation (13), Figure 9), directly reducing deployment and main  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Configuration** & **Nodes** & **Latency (s)** & **Total PLR (\%)** & **Energy (W)** & **Accuracy (\%)** \\ \hline Baseline (Mesh 2D) & 104 & 7.2 & 91.3 & 0.092 & 76.5 \\ \hline +Hybrid Topology (Non-dense) & 49 & 3.1 (\(-\)57\%) & 79.8 & 0.062 (\(-\)33\%) & 76.5 \\ \hline +Adaptive Routing & 49 & 1.8 (\(-\)75\%) & 70.5 & 0.070 & 76.5 \\ \hline +Dynamic Arbitration & 49 & 1.8 & 68.1 (\(-\)3.6\%) & 0.069 & 76.5 \\ \hline +AI Risk Prediction & 49 & 1.8 & 68.1 & 0.069 & 99.97 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation study (300 m radius, 60% fire diffusion).  
tenance overhead. Energy dissipation (Equation (15)) aligns with simulations (Figure 12): non-dense configurations minimize power (0.0425-0.0832 W) by leveraging \(O\Big{(}\sqrt{N}\)) routing, while dense topologies optimize the PLR through adaptive \(O\Big{(}\sqrt{N}\Big{)}\) rerouting.
## 5 Perceptual Audio Features Extraction Techniques
### Time Domain Perceptual Features
#### 5.1.1 Zero-Crossing Rate-Based Perceptual Features  
The following zero-crossing-based features which incorporate some bio-inspired auditory model can be found in the literature:  
* **Zero-crossing peak amplitudes (ZCPA):** were designed for automatic speech recognition (ASR) in noisy environments by [PERSON] _et al._[155], showing better results that linear prediction coefficients. This feature is computed from time-domain zero crossings of the signal previously decomposed in several psychoacoustic scaled subbands. The final representation of the feature is obtained on a histogram of the inverse zero-crossings lengths over all the subband signals. Subsequently, each histogram bin is scaled with the peak value of the corresponding zero crossing interval. In [156], [PERSON] and [PERSON] applied ZCPA to noise-robust speech recognition.
* **Pitch synchronous zero crossing peak amplitudes (PS-ZCPA):** were proposed by [PERSON] _et al._[157] and they were designed for improving robustness of ASR in noisy conditions. The original method is based on an auditory nervous system, as it uses a mel-frequency spaced filterbank as a front-end stage. PS-ZCPA considers only inverse zero-crossings lengths whose peaks have a height above a threshold obtained as a portion of the highest peak within a signal pitch period. PS-ZCPA are only computed in voiced speech segments, being combined with the preceding ZCPA features obtained from unvoiced speech segments. In [158], the same authors presented a new version of the PS-ZCPA feature, using a pitch-synchronous peak-amplitude approach that ignores zero-crossings.
## 2 Nonlinear, Rotor-Bearing System Structure  
### System Structure  
Considering the nonlinear contact force, the rotor-bearing system, placed horizontally with an unbalanced disc, is simplified (as the rotor is rigid) and supported by two symmetrical rolling bearings with the same parameters.  
A simplified dynamic model of the rotor system is shown in Figure 1a, while the schematic of the rolling bearing is shown in Figure 1b. In Figure 1a, \(x\)-axis is the horizontal direction, and \(y\)-axis is the vertical direction; and in Figure 1b, \(d_{s}\), and \(d_{r}\) are the diameters of the outer race and inner race, respectively, and \(\psi_{i}\) is the angle location of the \(i\)th ball.  
The dynamic differential equation of the rotor-bearing system in Figure 1 can be defined as:  
\[\left\{\begin{array}{c}m\ddot{x}+C\dot{x}+F_{x}=me\omega^{2}\sin(\omega t)\\ m\ddot{y}+C\dot{y}+F_{y}=me\omega^{2}\cos(\omega t)+F^{\prime}\end{array}\right. \tag{1}\]  
where \(m\) is the equivalent mass of the rotor and the inner race at the disc; \(C\) is the damping coefficient of the rotor at the disc and rolling bearing; \(\omega\) is the rotor angular velocity; \(F^{\prime}\) is the sum of the radial external force and the gravity of the rotor; \(e\) is the rotor eccentricity distance; and \(F_{x},\ F_{y}\) are the supporting force components in the \(x\) and \(y\) directions, respectively. According to Hertzian contact theory, the forces generated from the rolling bearing are defined as [28]:  
\[\left\{\begin{array}{c}F_{x}=K_{b}\sum\limits_{i=1}^{N_{b}}(y\cos\varphi_{i} +x\sin\varphi_{i}-\gamma_{0})^{1.5}\sin\varphi_{i}\\ F_{y}=K_{b}\sum\limits_{i=1}^{N_{b}}(y\cos\varphi_{i}+x\sin\varphi_{i}-\gamma _{0})^{1.5}\cos\varphi_{i}\end{array}\right. \tag{2}\]  
where \(K_{b}\) represents the Hertzian contact stiffness; \(\gamma_{0}\) is the rolling bearing radial clearance; and \(y\cos\varphi_{i}+x\sin\varphi_{i}-\gamma_{0}\) is the contact deformation between the \(i\)th ball and the races. Also, the \(i\)th ball angle location \(\varphi_{i}\) is:  
\[\varphi_{i}=\frac{2\pi}{N_{b}}(i-1)+\frac{d_{r}}{d_{r}+d_{s}}\omega t \tag{3}\]  
where \(N_{b}\) is the number of balls of the rolling bearing.
## 3 Basic Principle of Calculating Tropospheric Zenith Delay
### Model Accuracy Test  
In this paper, the average deviation (Bias) and root mean square error (RMSE) are used as the model accuracy evaluation criteria [24], and the calculation formula is as follows:  
\[Bias=\frac{1}{D}\sum_{d=1}^{D}\left(ZTD_{d}^{Model}-ZTD_{d}^{IGS}\right) \tag{11}\]  
\[RMSE=\sqrt{\frac{1}{D}\sum_{d=1}^{D}\left(ZTD_{d}^{Model}-ZTD_{d}^{IGS}\right) ^{2}} \tag{12}\]  
where \(ZTD_{d}^{Model}\) is the value calculated by VMF model interpolation in this paper; \(ZTD_{d}^{IGS}\) is the true value provided by the tropospheric product released by IGS; \(D\) represents the total number of observed values; Bias measures the average deviation between the calculated value of the model and the true value; RMSE measures the reliability of the model.
###### Abstract  
In pursuit of the Sustainable Development Goals (SDGs)'s objective of eliminating slum cities, the government of Indonesia has initiated a survey-based slum mapping program. Unfortunately, recent observations have highlighted considerable inconsistencies in the mapping process. These inconsistencies can be attributed to various factors, including variations in the expertise of surveyors and the intricacies of the indicators employed to characterize slum conditions. Consequently, reliable data is lacking, which poses a significant barrier to effective monitoring of slum upgrading programs. Remote sensing (RS)-based approaches, particularly those employing deep learning (DL) techniques, have emerged as a highly effective and accurate method for identifying slum areas. However, the reliance on RS alone is likely to encounter challenges in complex urban environments. A substantial body of research has previously identified the merits of integrating land surface data with RS. Therefore, this study seeks to combine remote sensing imagery (RSI) with street view imagery (SVI) for the purpose of slum mapping and compare its accuracy with a field survey conducted in 2024. The city of Bandung is a pertinent case study, as it is facing a considerable increase in population density. These slums collectively encompass approximately one-tenth of Bandung City's population as of 2020. The present investigation evaluates the mapping results obtained from four distinct deep learning (DL) networks: The first category comprises FCN, which utilizes RSI exclusively, and FCN-DK, which also employs RSI as its sole input. The second category consists of two networks that integrate RSI and SVI, namely FCN and FCN-DK. The findings indicate that the integration of RSI and SVI enhances the precision of slum mapping in Bandung City, particularly when employing the FCN-DK network, achieving an accuracy of 86.25%. The results of the mapping process employing a combination of the FCN-DK network, which utilizes the RSI and SVI, indicate the presence of 2294 light slum points and 29 medium slum points. It should be noted that the outcomes are contingent upon the methodological approach employed, the accessibility of the dataset, and the training data that mirrors the distribution of slums in 2020 and the specific degree of its integration within the FCN network. The FCN-DK model, which integrates RSI and SVI, demonstrates enhanced performance in comparison to the other models examined in this study.  
**Keywords:** urban slum mapping; remote sensing; street view imagery; deep learning; data fusion
# Optimal Design of Small-Aperture Optical Terminals for Free-Space Links
## 3 Results and Discussion
### Tip-Tilt Bandwidth Optimisation
\begin{table}
\begin{tabular}{c c c c c} \hline
**L [m]** & \(\mathbf{C_{n}^{2}[\text{m}^{-2/3}]}\) & **D [mm]** & \(\mathbf{f_{MB}[\text{Hz}]}\) & \(\mathbf{3\sigma_{jitter}[\text{nrad}]}\) \\ \hline \multirow{3}{*}{750} & \(1\times 10^{-15}\) & & \(<\)1 & 7.68 \\  & \(5\times 10^{-15}\) & 32.4 & \(<\)1 & 17.2 \\  & \(1\times 10^{-14}\) & & \(<\)1 & 24.2 \\ \hline \multirow{3}{*}{2400} & \(1\times 10^{-15}\) & & \(<\)1 & 12.5 \\  & \(5\times 10^{-15}\) & 57.9 & 4.74 & 17.8 \\  & \(1\times 10^{-14}\) & & 15.8 & 39.4 \\ \hline \multirow{3}{*}{10,600} & \(1\times 10^{-15}\) & & 11.9 & 23.0 \\  & \(5\times 10^{-15}\) & 122 & 42.3 & 51.5 \\ \cline{1-1}  & \(1\times 10^{-14}\) & & 63.1 & 72.9 \\ \hline \end{tabular}
\end{table} 
Table 2: Required TT bandwidth, \(f_{3 dB}\), and range, \(3\sigma_{jitter}\), for a given link length and turbulence condition based on the optimal aperture size, \(D\), from Table 1.  
Figure 6: Residual jitter values as a function of TT suppression bandwidth for different aperture diameters. Three blue curves for different turbulence conditions (Fried parameters) are shown. The dashed black curves correspond to the maximum residual jitter permitted for different \(D\) so that \(\tilde{\eta}_{c,TT}\) remains above 90%. the TT compensation bandwidth, yielding a coupling efficiency very close to the TT-compensated curves in Figure 3.  
It should be clarified that the value of \(\tilde{\eta}_{c,TT}(\sigma_{jitter})\) is not an indication of the achievable increase in coupling efficiency if all TT is compensated. While \(\eta_{c}\) can be decomposed into contributions from Zernike modes via Equations (4) and (5), the relationship between the residual jitter and the corresponding \(Z_{2}\) and \(Z_{3}\) coefficients is unclear. As such, it is difficult to relate an arbitrary amount of jitter to \(\eta_{c}\) when other wavefront perturbations are present. This means the uncompensated \(\eta_{c}\) for a dynamic FSO link is not producible with this analysis. However, as the TT-compensated curves from Figure 3A remain valid, following this analysis for a dynamic FSO link will still properly constrain the TT compensation system to achieve the optimal power throughput.
## 3 Results of the PLS-SEM Path Model Estimation
### Assessing the PLS-SEM Results of the Reflective Measurement Model  
No collinearity problem concerning the outer model was detected. The VIF (variance inflation factor) values ranged from 1.000 to 2.013. Only values of 5.000 or above would indicate a problem [23].  
For the assessment of the internal consistency, three criteria must be met. First, each Cronbach's alpha (CA) measurement should fall above 0.7 but below 0.95. Second, the composite reliability (CR) measurements should exceed 0.7. Third, rho_A, which offers an average value between CA and CR, should be greater than 0.7 [23].  
The assessment of convergent validity requires that the average variance extracted (AVE) be greater than 0.5 [23]. Table 6 shows all the values.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Construct** & **Indicator** & **Description** & \begin{tabular}{c} **Outer** \\ **Loading** \\ \end{tabular} & **VIF** & \begin{tabular}{c} **Cronbach’s** \\ **Alpha** \\ \end{tabular} & **CR** & \begin{tabular}{c} **rho\_A** \\ **AVE** \\ \end{tabular} \\ \hline \multirow{3}{*}{DESEMP} & DESEMP-1 & \begin{tabular}{c} Recycling rate \\ DESEMP-3 \\ Availability of plastic settling \\ technologies \\ \end{tabular} & 0.783 & 1.489 & 0.775 & 0.868 & 0.798 & 0.688 \\  & \begin{tabular}{c} DESEMP-4 \\ \end{tabular} & \begin{tabular}{c} Business profitability \\ Availability of plastic settling \\ technologies \\ \end{tabular} & 0.866 & 1.626 & & & & & \\ \hline \multirow{3}{*}{EFICA} & EFICA-1 & Market maturity & 0.880 & 1.508 & 0.734 & 0.883 & 0.737 & 0.790 \\  & EFICA-3 & Volume of processing & 0.897 & 1.508 & & & & & \\ \hline \multirow{3}{*}{EFICI} & EFICI-3 & Variability of waste & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\  & \begin{tabular}{c} INTRA-1 \\ INTRA-2 \\ \end{tabular} & \begin{tabular}{c} Availability of selective collection in \\ the municipality of Deposit-Return Systems \\ \end{tabular} & 0.945 & 2.013 & 0.830 & 0.920 & 0.879 & 0.852 \\  & \begin{tabular}{c} INTRA-2 \\ \end{tabular} & \
\begin{tabular}{c} Availability of Deposit-Return Systems \\ \end{tabular} & 0.901 & 2.013 & & & & & \\ \hline \multirow{3}{*}{SOCIO} & SOCIO-1 &
\begin{tabular}{c} Socieconomic profile of the \\ municipality \\ \end{tabular} & 0.896 & 1.425 & 0.706 & 0.872 & 0.715 & 0.772 \\  & SOCIO-2 & Population density of the municipality & 0.862 & 1.425 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Outer loadings, Cronbach’s alphas, composite reliabilities, rho_A, and average variance extracted values.  
Figure 5: Values of the obtained path coefficients and outer loadings with the second run after the removal of indicators below the outer loading threshold.