esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Data Acquisition and Processing  
### Ambient Vibrations  
A single station ambient vibration measurements campaign was conducted in the city of Oued-Fodda with a set of 125 points. For reliable results, the measurements were made following the advice found in the SESAME report [36]. The maximum distance between 2 adjoining stations was about 300 m. One hundred and two (102) recordings were selected for the analysis (Figure 3), the remaining recordings were excluded due to bad quality signals.  
The distribution of the measurement points depends mainly on the different geological formations. Recordings were densified in the city center, on the bedrock knoll. Since the city is mainly growing southward, the recordings were also densified in the southern part (Figure 3).  
A couple of TROMINO seismographs were used for the measurements, with a sampling rate of 512 samples per second. Due to the daytime traffic and human activity, measurements were performed during night time within the city center with an acquisition time of 20 min. In the areas outside the city, which mainly consists of farms and agricultural lands, acquisition time was between 20 min and 40 min, depending on human activity and weather conditions.  
The data processing has been carried out using the Geopsy software ([[http://www.geopsy.org](http://www.geopsy.org)]([http://www.geopsy.org](http://www.geopsy.org))) [34]. All the recorded signals has been processed in the same manner: They have been divided into 5% cosine-tapered 40 s windows and the corresponding amplitude spectra have been smoothed using the Konno-Omachi algorithm [37] with a smoothing coefficient of 40. As a result, an HVSR curve is obtained by averaging the individual HVSR ratios of all the selected windows. For the consistency of the results, we have followed the criteria for clear and reliable experimental curves as recommended in the SESAME report [36].
## 5 Simulation Calculation Model of Grouting
### The Impact of Various Factors on the Slurry Diffusion Radius  
#### 5.4.1 The Influence of Different Grouting Pressure on Slurry Diffusion Radius  
To isolate the influence of individual parameters on slurry diffusion characteristics, a series of controlled single-factor experiments were conducted. Under constant conditions of drilling speed (0.3 m/min) and slurry viscosity (138.9 mPa-s), the grouting pressure was systematically varied from 0.1 to 0.5 MPa to investigate its specific effects on diffusion radius. The resultant pressure-dependent diffusion patterns are presented in Figure 9.  
Based on regression analysis of the results, the correlation between the slurry diffusion radius and grouting pressure is established as follows:  
\[\begin{cases}R=1.286p^{0.753}\\ r^{2}=0.987\end{cases} \tag{10}\]  
From the Equation (10), it can be seen that the slurry diffusion radius has a power function relationship with the grouting pressure, and the slurry diffusion radius increases with the increase of the grouting pressure. When the grouting pressure is 0.1 MPa, the slurry diffusion radius is 24.8 cm. When the grouting pressure increases to 0.3 MPa, the slurry diffusion radius is 52.6 cm, which is expanded by 112.1%. When the grouting pressure increases from 0.3 MPa to 0.5 MPa, the slurry diffusion radius is 78.4 cm, which is expanded by 49.1%. It can be seen that the slurry diffusion radius will increase with the increase of grouting pressure, but the rate of increase will gradually decrease. Therefore, too large grouting pressure will not significantly increase the slurry diffusion radius. Field applications require adjustments for anchor length and in situ heterogeneity.  
Figure 10 presents the slurry diffusion patterns under varying grouting pressures, revealing two key phenomena: (1) the diffusion radius exhibits positive pressure-dependence  
Figure 9: The correlation between the slurry diffusion radius and grouting pressure.  
while maintaining downward progression, and (2) pronounced slurry concentration gradients emerge, with the grouting port region showing significantly higher density than peripheral zones. Notably, the observed diffusion demonstrates preferential lateral expansion--a deviation from conventional vertical-dominated penetration patterns. While laboratory-scale constraints may accentuate this lateral effect, future work will investigate scale dependencies through either three-dimensional numerical simulations or field-scale validation tests.
## 2 Numerical Methods
### Spherical Bubble Dynamics  
Among various types of cavitations observed in an underwater propeller, wing-tip vortex cavitation occurs first as the operating speed increases. Therefore, to design low-noise and high-performance underwater propellers, the process of creation of wing-tip vortex cavitation should be closely examined. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \(R_{0}\) (\(\mu\)m) & 30 & 40 & 50 & 60 & 70 & 80 & 90 & 100 \\ \hline \(p_{\sigma}\) (Pa) & 725 & 924 & 1030 & 1094 & 1137 & 1167 & 1189 & 1206 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Critical pressure according to initial nuclei size. Figure 6: Intermolecular potential versus distance, \(x\), between molecules [28]. As described in the introduction, the RANS solvers with a homogeneous mixture model have been frequently used for the cavitation simulations of hydrofoils and propellers. Specifically, many studies have been conducted on sheet cavitation and tip-vortex cavitation, and the predicted results exhibit qualitative agreements with the measured data in terms of the overall shape of cavitation [19; 20; 21; 22]. To define the CIS, [PERSON], et al. [43] computed the minimum pressure and volume of tip-vortex cavitation and visually compared the cavitation tendency with the experiment. However, it was reported that the cavitation tendency differs between the experimental and numerical results, and the difference increases when the body shape is complicated, such as ducted propellers. Furthermore, [PERSON] et al. [44] suggested that although the homogeneous mixture model accurately predicts the macroscopic behavior of bubbles, local oscillations within macroscopic movements can be accurately predicted using the bubble dynamics. Although there are no generally accepted criteria to define the CIS, two approaches are frequently used. The first approach involves a visual approach where the size of bubbles is visually observed, and the second approach involves an acoustic approach where the sudden increase in noise level is used as an indication of the cavitation inception [29]. Therefore, in this study, wing-tip vortex cavitation is simulated using the Lagrangian approach, where the bubble dynamics model is used under the assumption of the spherically symmetric bubble. The advantage of using the bubble dynamics model is that it can effectively track the volume variation of bubbles and the noise due to the volumetric bubble acceleration, which can be used as a visual and acoustic index of cavitation inception, respectively. The volume variation of the bubble can be predicted using the Keller-Herring equation, where the bubble radius is determined with the given external information such as external pressure outside the bubble. The location of the bubble can be traced by solving the trajectory equation with the given flow information around the bubble. The Keller-Herring equation includes terms that reflect liquid compressibility and consider energy dissipation during a volume change, while the Rayleigh-Plesset equation maintains the energy as constant under the assumption of incompressible flow [28; 45].
### Doppler Effect and Filter  
When a fixed frequency wave hits a moving object, it will reflect the wave with a different frequency based on the relative velocity of the object to the wave's transmitter. Suppose both object and RADAR have a velocity far inferior to the electromagnetic wave's speed. In that case, the frequency's shift is directly proportional to the relative velocity of the object relative to the emitter, and it is called the Doppler effect [37].  
PondView  
The PondView diagram [38] is a method to visualize and interpret GPR maps; this article will use this type of diagram for RADAR data. It allows the user to better identify buried objects and assess how deep the feature is.  
PondView diagrams use thin horizontal slices taken from a map. They are usually only a few centimeters thick, containing the information of just one measurement from the GPR-like sensor. A thick slice is also possible by combining successive thin slices that can be vertically averaged or substituted by the vertical maximum.  
PondView takes advantage of thick slices using the maximum method. It colorizes the thick slice with a two or three-color matrix based on the depth of each maximum. For the two-color pondView, the green is for shallower objects and blue is for deeper objects. The intensity of the color on each cell is based on the values of the measurements that need to be displayed; it is common that the actual data to be shown are based on differences to clarify the relevant data.  
Summarizing, the PondView diagram deals with data that are 4D; that is, for each 2D cell (seen from above), the signal intensity over depth is at stake (a uni-dimensional vector of scalars). The shown color in each cell is related to the maximum difference of measurements just above and just below the depth at stake; saturated colors (far from black) relate to larger differences and, in turn, imply a medium change at that depth [38].
# Semantic Segmentation of Packaged and Unpackaged Fresh-Cut Apples Using Deep Learning
## 1 Introduction
Few researchers have reported interesting results about the use of CVS through packaging [28; 29]. In this work, we aim to segment images of fresh-cut apples by separating the pulp from peel. This step is preliminary to classification and regression tasks that will be based on colour analysis, and they are beyond the scope of this paper. In particular, we will be interested in measuring the browning occurring on the internal part on the fruit (pulp) due to senescence. The experiments of semantic segmentation described in this paper were accomplished by acquiring images of fresh-cut apples belonging to four different varieties: Gramny Smith, Golden, Fuji, and Pink Lady. One of them (Granny Smith) has a greenish peel, another has a yellowish peel (Golden), while the other two have a mostly reddish peel. The segmentation must be able to separate pulp regions in all these varieties even through the packaging. On packaged products, it needs to recognize peel and pulp pixels belonging to regions of the images whose colors are meaningfully visible in spite of the photometric deformations introduced by the interaction between the light and the plastic bag. While acceptable results can be obtained by traditional methods on greenish and reddish peels, a yellowish peel strongly degrades the performance of pulp identification. We needed a flexible and powerful technique to isolate the pulp even on these critical images in a robust way. To increase the robustness of the evaluation of apple quality and of internal properties, it is important to minimize errors introduced by pixels that are misclassified as pulp, whose number must be kept as low as possible. A deep learning CNN model for semantic segmentation of fresh-cut apples has been designed, implemented, and tested in the MATLAB environment. Four classes have been defined: peel, pulp, background, and glittering. The first three are used for both packaged and unpackaged apples while the last one is used only when the apples are observed through the bag. The proposed solution is based on the DeepLabV3+ architecture that has been chosen owing to its accuracy in segmentation problems [30]. The developed semantic segmentation achieved an accuracy greater than 99% on images of both packaged and unpackaged products. The approach has been purposely made conservative to satisfy the requirements of the subsequent analysis. Proper choices have been made while training the network and choosing the evaluation metrics to be coherent with the decision of preferring to lose some pixels of the pulp instead of wrongly including them in color analysis parts of the image that belong to other classes (peel, background, glittering). The approach proved to be useful to isolate the parts that are affected by the browning process and that are better suited to evaluate the internal maturity and the properties of interest.
# Dynamic Infection Spread Model Based Group Testing
## 2 Related Work  
While the standard group testing models consider the one-shot infection status identification problem and do not consider dynamic infection status changes in the system over time, following works are related to our proposed model closer than the classical static group testing works. [PERSON]. [32] considers a limited identification scenario in a classical setting, addressing the limited test capacity with partial identification cases for real-life scenarios. In a continuous setting and without focusing on explicit testing algorithms, but approaching the problem as a control system with controlled variable testing rate, [PERSON]. [33] considers a dynamic testing infection spread model based on the well-known SIR model [34]. However, the first works that focus on the testing of individuals in a dynamic setup with an emphasis on group testing are [35; 36], where the authors consider a delay in test results and minimize the number of required tests to identify the infection status of the population at each time instance, stating an equivalence to the classical static group testing problem. Despite the fact that the tests are performed to identify the infection status of everyone in the population, new infections are introduced into the system, due to the assumed delay in test results, i.e., during the time between performing the tests and getting the test results. In a more recent work, [PERSON]. [37] considers a scenario where the tests are done in a dynamic manner, with a focus on two-step Dorfman testing with delay and disease spread between the two steps, in a discrete SIR model with a community structure; the authors in [37] consider quarantining the possible infections, and analyze the trade-off between quarantine and test costs.  
In this paper, we consider a dynamic disease spread model which is motivated by the discrete-time SIR model, similar to the disease spread model presented in [35]. Distinct from the works that we discuss in this section, we propose a system model where the testing capacity is limited at each time instance and only partial identification can be done at each time instance, while disease keeps spreading over time.
# Overview on Different Types of Solar Cells: An Update
## 5 Thin-Film Solar Cells
### CuInX (X = S, Se, and Te) Film Solar Cells  
Several deposition methods have been used to produce CuInSe\({}_{2}\), CuInTe\({}_{2}\), and CuInS\({}_{2}\) films (Table 5). It is noted that the non-vacuum deposition method offers an attractive cost-saving opportunity, higher deposition speed, and less waste of chemicals when compared with the vacuum-deposition technique. The obtained films could be employed for solar-cell applications because of long-term stability under solar radiation, excellent absorption coefficients (10\({}^{5}\) cm\({}^{-1}\)) in the visible light portion, and suitable band gap values (1.5 eV).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Deposition Technique**} & \multicolumn{4}{c}{**Photovoltaic Properties**} & \multirow{2}{*}{**Reference**} \\ \cline{2-2} \cline{4-6}  & **V\({}_{\text{oc}}\) (mV)** & **I\({}_{\text{sc}}\) (mA/cm\({}^{2}\))** & **PEC (\%)** & **FF (\%)** & \\ \hline Pulsed-laser deposition & 376 & 38.9 & 5.85 & 40 & [208] \\ \hline Chemical route & 521 & 19.13 & 6.17 & 62 & [209] \\ \hline Nanoparticle ink technique & - & - & 2.1 & - & [210] \\ \hline Electro-deposition method & 563 & 14.8 & 3.4 & 41 & [211] \\ \hline Pulsed hybrid electro-deposition method & 350 & 3.9 & 0.59 & 43 & [212] \\ \hline Sputtering method & - & - & 2.95–3.59 & - & [213] \\ \hline SILAR method & - & - & 0.76–1.24 & - & [214] \\ \hline reactive-pulsed dc magnetron co-sputtering & 667 & 19.6 & 7.9 & 60 & [215] \\ \hline Electro-deposition method & 204 & 7.6 & 0.55 & 35.68 & [216] \\ \hline Spin-coating method & - & - & 0.5–2.4 & - & [217] \\ \hline magnetron sputtering method & 666 & 7.9 & 2.26 & 43.39 & [218] \\ \hline Spin-coating method & 556 & 13.5 & 3.02 & 40.3 & [219] \\ \hline Spray pyrolysis & 246–421 & 3.9–10.3 & 0.4–1.4 & 32.5–39 & [220] \\ \hline Spin-coating method & 520 & 112.52 & 0.28 & 26.75 & [221] \\ \hline Pulsed-laser deposition & - & - & 6.62 & - & [222] \\ \hline Sulfurization technique & 610 & 12 & 4.5 & 62 & [223] \\ \hline \end{tabular}
\end{table}
Table 4: The growth of CZTS films using various deposition methods and the photovoltaic properties.  
Figure 12: Crystal structures of (**a**) stannite and (**b**) kesterite [207].
# Mobility-Enhanced Reliable Geographical Forwarding in Cognitive Radio Sensor Networks
## 5 Mror: Mobile Reliable Opportunistic Routing for CRSN  
This section presents the details of MROR operational blocks. MROR is made up of a route request module, a route selection module, a VCG formation module, a VMH zoning strategy, a VCG initiative determination forwarding (VIF) and route management modules.
## Materials and Methods  
### Enrollment of Patients  
A total of 30 patients (16 males and 14 females, aged 27 to 79 years) who needed to undergo extraction were enrolled in this study. The study aims and methods were explained to the patients, and written informed consent was obtained in advance. The study protocol was approved by the Institutional Review Board of Korea University Anam Hospital (IRB number: MD 15013, Registration date: 15 January 2016). The study was performed at the Department of Dentistry, Korea University Anam Hospital. The inclusion criteria were healthy individuals aged 18 or older who needed to undergo extraction of one or more third molars and hopeless teeth (premolar and molar). Reasons for extraction were root fracture, failure of root canal treatment, extensive caries, and moderate to severe periodontitis involving less than 50% of vertical bone loss. The following patients were excluded: those with unstable systemic diseases, uncontrolled metabolic diseases, or a habit of smoking \(\geq\)10 cigarettes/day; those who had been administered antibiotic drugs or corticosteroids within the last 3 months or who were receiving medication that altered bone healing, such as bisphosphonates; and those with a history of chemotherapy or radiative therapy performed in the oral and maxillofacial region.  
Each of the extraction sockets were randomly assigned into one of three therapeutic groups (Figure 1):  
1. Group A: Bio-Oss(r) Collagen (Geistlich, Wolhusen, Switzerland) graft (\(n=10\))
2. Group B: DDM graft (\(n=10\))
3. Group C: DDM graft combined with rhBMP-2 (rhBMP-2/DDM) (\(n=10\))
## 3 Survey of Techniques for Updating
### The Execution Environment
#### 3.3.24 Zephyr  
Zephyr [78] improves code-size reduction by performing application-level modifications to the updated code, which reduces the size impact of function shifts. A modified version of the _rsync_[41] algorithm is used to generate a differences script. Dissemination has two stages: first all the nodes are requested to reboot the _reprogramming component_ using flooding, then the _Stream_ approach is used to distribute the script to all nodes. After the script has been applied to build the new image, and as the new image is loaded into program memory, any indirect function calls (_i.e._, using a function call table) are replaced by direct calls to improve performance. The nodes then reboot into the new image.  
Zephyr has no specific support for autonomic features, but the authors report a very significant reduction in code size, which makes updates more flexible in general.
# Machine Learning Prediction of Airfoil Aerodynamic Performance Using Neural Network Ensembles
## 2 Methodology
### Seed Randomness
#### 2.3.1 Seed Influence on Weights and Biases  
The Glorot Uniform Initialization method [(29)] is a widely used weight initializer that stabilizes the training of deep networks by keeping the variance of activations approximately constant across layers. Weights are drawn from a uniform distribution:  
\[W_{ij}\sim u\ \left[-\frac{\sqrt{6}}{\sqrt{n_{in}+n_{out}}},\frac{\sqrt{6}}{ \sqrt{n_{in}+n_{out}}}\right] \tag{10}\]  
where \(n_{in}\) represents the number of inputs, \(n_{out}\) represents the number of outputs, and \(u\) (continuous uniform distribution) represents a probability distribution where every number in the interval has an equal chance of being picked. Equation (10) can be rewritten by a variable, a, which defines the range of values from each weight \(W_{ij}\), which is sampled as follows:  
\[W_{ij}\sim u\ (-a,+a) \tag{11}\]  
After the Glorot limit a in Equation (11) is computed, the PRNG is initialized to a specific state given the seed that is allocated (Group A/B). PRNG is a deterministic function that takes seeds to initialize an internal state, and then produces a sequence of outputs:  
\[\text{PRNG}_{\text{s}}:\mathbb{N}\longrightarrow[0,1) \tag{12}\]  
The function produces a list of values generated deterministically from seed s:  
\[\text{PRNG}_{\text{s}}=\left[\psi_{0}^{(s)},\ \psi_{1}^{(s)},\psi_{2}^{(s)},\ \ldots\right] \tag{13}\]  
where each \(\psi_{k}^{(s)}\in\ [0,1)\) is the k-th number in the sequence generated under seed s. These values are then linearly transformed into the Glorot range:  
\[W_{ij}^{(s)}=2a\cdot\psi_{k}^{(s)}-a\ \in(-a,+a) \tag{14}\]Just like weights, biases in each layer are initialized using PRNG seeded with a fixed value s:  
\[b_{i}^{(s)}=2a\cdot\psi_{i}^{(s)}-a\ \in(-a,+a) \tag{15}\]  
PRNGs are generated as massive deterministic sequences, and their values are consumed in order throughout the algorithm to train the model. Each PRNG call consumes one float and is used exactly once per weight/bias. When the same seed is used for model training, the entire stochastic process, including weight initialization, data shuffling, and batch ordering, becomes deterministic, leading to identical results. However, changing the seed alters this pseudo-random trajectory, producing different outcomes. In this current work, Philox PRNG is used, which supports up to \(2^{64}\) distinct outputs per seed. The random seed itself is a 32-bit unsigned integer, allowing for values from 0 to \(2^{32}-1\), providing over four billion unique initialization states. Each time a new weight or a bias is needed, Philox uses the defined seed s and an incrementing internal counter to obtain new random values (the same principle as in Equation (13)). The following Table 1 illustrates how the chosen seed influences the initialization of weights and biases across the entire machine learning architecture, as previously described in Section 2.1. In total, 282,210 PRNGs are used for single seed configuration models on initializing weights and biases.
## 5 Experimental Results
### Statistical Analysis  
To validate the results from the Shannon-Hartley equation, a correlation factor is calculated between the calculated channel capacity and the measured FER percentage. The Pearson's correlation coefficient is one of the common tool used to analyze the linearity between the parameters [31].  
The correlation factor analysis results are presented in Table 2. The correlation factor is calculated for the data obtained using the three different modulating signal patterns. For the sawtooth signal, the correlation factor between the channel capacity and FER is \(-0.954\), indicating a strong linear relationship--as the channel capacity increases, FER decreases accordingly. In the case of the sine wave signal, the correlation factor is \(-0.6551\), which, although lower than that of the sawtooth, still reflects a significant negative linear correlation. On the other hand, for the PAM signal, the correlation factor is 0.07009, indicating that there is no meaningful relationship between the calculated channel capacity and FER in this case.