esa-sceva/satcom-chunk-collection huggingface dataset:
# Cosmo ArduSiPM: An All-in-One Scintillation-Based Particle Detector for Earth and Space Application
## 3 Overview of the Discrete Analog Functional Blocks of ArduSiPM
### Optical Test
#### 4.2.3 ADC Signal Amplitude Spectrum  
The SAMV71 microcontroller is equipped with a configurable 12-bit ADC that allows measurements of signal amplitude thanks to the Cosmo ArduSiPM peak hold (Section 3.5). In our configuration, the maximum achievable resolution for the ADC is 0.2 mV. Since the SiPM can only deliver pulses with amplitude levels that are multiples of the amplitude generated by a single switching-on cell, if the resolution is sufficient to distinguish  
Figure 12: Threshold scan of the used SiPM in dark conditions. The ladder structure gives reliable results on dark frequency for each coincidence count.  
between two adjacent amplitude levels, it means we can accurately determine the number of cells that have switched on and thus extract all the available information from the pulse amplitude.  
In this context, we investigated the system's ability to distinguish between two consecutive peaks of the amplitude histogram using the same LED source settings. The obtained results are compared with those from an analog study conducted using a 12-bit, 400 MHz, 2 GS/s oscilloscope Lecroy WaveRunner HRO 64 Zi (Figure 13). Despite the significant difference in acquisition numbers, primarily caused by the longer dead time of the oscilloscope compared with our system, both devices yield a similar measurement of approximately 3 mV between consecutive peaks of the amplitude histogram.  
The analog signal was picked up from Cosmo ArduSiPM analog output, showing the SiPM signal after the first amplification stage (Figure 14).  
Like every ArduSiPM detector, Cosmo ArduSiPM measures the time between consecutive signals as the number of the MCU (Microcontroller Unit) clock pulses; this means 6.6 ns is the limit for the SAMV71 microcontroller family. The measured jitter between the LED pulse and the TTL output of the Cosmo ArduSiPM is around 600 ps, around one order of magnitude lower than the highest possible resolution.  
Figure 13: Comparison between amplitude histogram produced with the Cosmo (**top**) and the LeCroy Oscilloscope (**bottom**). The gain is defined as the distance between two consecutive peaks.
## 4 Proposed Estimation Scheme
### Estimation of Desired Power, CCI Power, and COR
#### 4.1.2 Estimation of Power  
\(\eta_{I,A}\) includes the components of the desired signal, noise, and CCI under the access of the SC system. \(\eta_{I,A}\) and \(\eta_{I,B}\) are as follows.  
\[\eta_{I,A} =\widehat{P_{D}}+\widehat{P_{n}}+\lambda\widehat{P_{I}}+\delta_{A}, \tag{11}\] \[\eta_{I,B} =\widehat{P_{n}}+\lambda\widehat{P_{I}}+\delta_{B}. \tag{12}\]  
where \(\widehat{P_{D}},\widehat{P_{n}}\), and \(\widehat{P_{I}}\) are the estimated power of the desired signal, noise, and CCI, respectively. \(\lambda\in\{0,1\}\) is an indicator that specifies the access conditions for the SC system. When the SC system is accessing and not accessing, \(\lambda\) has 1 and 0, respectively, and \(\delta_{A}\) and \(\delta_{B}\) are residual components. The number of partial spectra is so significant that \(\delta_{A}\) and \(\delta_{B}\) are negligible.  
When \(\eta_{I,B}\) is subtracted from \(\eta_{I,A}\), the noise and CCI components are reduced. If the residual components \(\delta_{A}\) and \(\delta_{B}\) are ignored, the power of the desired signal is estimated [45]. To estimate the power of the noise and CCI, The access condition of the SC system should be specified to estimate the power of noise and CCI. We decompose the noise and CCI from \(\eta_{I,B}\).
## 4 Effect of OCR on Pore Pressure Dissipation  
### Effect of OCR on Initial Pore Pressure  
The computed excess pore pressure \(\Delta u\) contours after penetration and before dissipation for different OCRs at \(\sigma_{\rm v}=30\) kPa in kaolin silty clays are shown in Figure 6. The excess pore pressure \(\Delta u\) contours of the Yellow River Delta silty clay was almost the same. The size of the pore pressure influence zone changed little. However, the gradient of the pore pressure increased with the increase in OCR. The pore pressure along the radial direction at the cone shoulder (\(u_{2}\)) increased before it decreased. As the OCR increased, the changes became more dramatic.  
Figure 6: Computed \(\Delta u\) contours after penetration under different OCR at \(\sigma_{\rm v}=30\) kPa in kaolin silty clays. (**a**) OCR = 1; (**b**) OCR = 3; (**c**) OCR = 5.  
By comparing the \(\Delta u\) contours in Figure 7, it apparent that the variation range of the pore pressure in the horizontal direction is higher than that in the vertical direction. This observation suggests that the soil's anisotropic behavior, i.e., the difference in its mechanical properties and behavior in different directions, is likely to be a factor in the pore pressure distribution. The dissipation of excess pore pressure in soil during consolidation can occur in different ways depending on the soil properties, boundary conditions, and loading conditions. One factor that affects the dissipation of excess pore pressure is soil properties. The type and characteristics of the soil, such as its permeability, compressibility, and particle size distribution, can influence the rate and mechanism of pore pressure dissipation. At the cone shoulder position (\(u_{2}\)), the dissipation of the excess pore pressure was horizontal radial diffusion, corresponding to the problem of axisymmetric consolidation as per Equation (20), and the distribution of the initial pore pressure field affected the solution of Equation (20), so the shape of dissipation curve changed.  
\[c_{h}(\frac{\partial^{2}u}{\partial r^{2}}+\frac{1}{r}\times\frac{\partial u}{ \partial r})\ =\ \frac{\partial u}{\partial t} \tag{20}\]  
where  
\[c_{h}\ =\ \frac{2 kG(1-v)}{\gamma_{w}(1-2v)} \tag{21}\]
## 3 Circuit Design
### Digital Signal Processing
Each AP is transmitted, once completely stored, in \(176\)\(\mu\)s and thus the worst-case latency time (time difference between the detection and the transmission of an AP) is about \(2.9\) ms, which is not an issue both in neuroscience experiments and in future prosthetic applications [23]. The validity of the data-reduction algorithm was tested by running a custom clustering software that employed fuzzy c-means spike sorter [24, 25] on a data set from a public source [26] with spike waveforms hardly distinguishable one from each other (see Figure 9). The ratio between the peak-to-peak spike amplitude and the rms noise was approximately \(10\). Clustering based on Principal Component Analysis (PCA) was performed on original data containing spikes from three different  
Figure 8: Missing spike percentage as a function of the average firing rate per channel for different memory sizes and for a read speed of \(1\) Mbit/s. neurons and on the same data but reduced according to the above windowing strategy. Figure 9 shows that clustering on the reduced data set leads to clear separation of the three spike families, with identification errors reported in Table 2. Two types of error occur in a PCA-based clustering: type I errors occur when APs from two different neurons are grouped together (false positives), whilst type II errors occur when not all APs generated by one neuron are grouped together (false negatives). When applied to the original dataset, the PCA-based identification procedure leads to type I/type II mean error rates of \(4.1\%\)-\(5.2\%\), respectively. The reduced data sets showed \(4.6\%\)-\(5.3\%\) values, thus demonstrating that the reduction strategy preserves the quality needed for effective neuron identification. These results are not surprising considering that, as stated in [22], more than \(80\%\) of neural spike information is carried by three analog features of the AP waveform, namely the peak and the through amplitudes and the time between them. The results of spike sorting using these analog features [27] and performed on the same trace are also reported in Table 2. Note that the compression method employed in this work always outperforms the clustering obtained using only these three features. Finally, it is worth nothing that the current consumption of the overall digital signal processing is limited to about \(400\)\(\mu\)A, most of which consumed by the finite-state machine. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**AP class** & \multicolumn{2}{c}{**Original**} & \multicolumn{2}{c}{**Reduced**} & \multicolumn{2}{c}{**Peak-trough-width**} \\ \hline Error type & I & II & I & II & I & II \\ \hline A (379) & 4.1\% & 6.9\% & 3.1\% & 2.9\% & 23\% & 22\% \\ B (389) & 2.1\% & 3.3\% & 3.7\% & 6.9\% & 12\% & 18\% \\ C (401) & 6.2\% & 5.5\% & 6.9\% & 6.0\% & 17\% & 30\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Sorting results. Figure 9: Waveforms of neural spikes and PCA-based clustering results employing the proposed windowing strategy, _i.e._, processing \(20\) samples after the threshold crossing.
## 3 Results and Discussion
### Quantitative Analysis of Soil Microstructure
#### 3.2.6 Fractal Dimension of Pores  
Previous studies have shown that soil particles and pores can be described by the fractal dimension [45; 46]. The fractal dimension is used to characterize the distribution of pores and to deeply analyze the deformation features of soil samples under the action of lateral consolidation compression. The area-perimeter method is used for the calculation of the fractal dimension, and its basic principle is as follows:  
\[\log(L)=(G/2)\log(A)+C \tag{5}\]  
where \(L\) is the perimeter of the pore; \(A\) is the area of the pore; \(C\) is a constant; and \(G\) is the fractal dimension of the pore. The general value range of \(G\) is [1; 2], and as \(G\) becomes larger, the pore structure becomes more complex.  
Figure 11 shows the fitted curves of the fractal dimension of each experimental group according to Equation (5). Then, the fractal dimension of each group was calculated, which was plotted against consolidation time, as shown in Figure 12. Figure 12 shows the curve of the fractal dimension of soil pores under different consolidation times. Based on the above microstructure investigation, the curve of the fractal dimensions was also separated into three stages. When compared to before consolidation, soil samples after remolding treatment exhibited good homogeneity and simple pore structures. During the initial stage of consolidation, large soil particles were crushed and divided into small particles, and the development of small soil particles resulted in the formation of small holes between the  
Figure 10: A wind rose diagram of the directional frequency of pores under different consolidation times.  
small particles [41]. At this time, the pore structure became more complicated, and the fractal dimension grew. Then, the soil microstructure began to be reconstructed, and soil particles moved continuously under the action of consolidation compression to adjust the microstructure and to gradually evolve to a new equilibrium state. The fractal diameter of the pores decreased over time. As a result of consolidation, the area of the pores decreased, the shape became smoother, the degree of the homogeneity of the pores rose and the complexity of the pore structure was reduced. At the later stage of consolidation, the change in the microstructure of the soil slowed down, and the fractal dimension of pores varied only a little. The microstructure was restructured and eventually reached a new equilibrium.  
Figure 11: Fitted curves of the fractal dimension of each experimental group under different consolidation times: (**a**) 0 min; (**b**) 30 min; (**c**) 60 min; (**d**) 240 min; (**e**) 720 min; (**f**) 1440 min.
###### Abstract  
In Southwest China's high-temperature, humid, and rainy climate, ancient sandstone structures face significant deterioration due to acid rain and water accumulation, which cause dynamic and static dissolution. This degradation weakens the sandstone's physical and mechanical properties, threatening the preservation of cultural heritage sites. Dynamic dissolution is the process of matter and energy exchange during fluid-rock or fluid-mineral interactions under dynamic conditions. Under dynamic conditions, continuously renewed fluids supply chemicals for dissolution and remove dissolved products, sustaining reactions similar to acid rain dissolution. Static dissolution is the dissolution-erosion process between fluids and rocks or minerals in a relatively stationary fluid environment. Unlike dynamic dissolution, which involves moving fluids, static dissolution occurs in nearly stagnant fluids, where rising product concentrations from acid-rock reactions may hinder further dissolution, akin to static immersion dissolution. This study systematically examined how different dissolution conditions affect sandstone's pore structure, mechanical properties, and hygroscopic behavior. Nuclear magnetic resonance (NMR) and scanning electron microscopy (SEM) were used to analyze pore structure changes, while ultrasonic testing and Leeb hardness measurements assessed mechanical strength. Hygroscopicity was evaluated through non-destructive moisture testing in controlled environments. The results show that dynamic dissolution has a greater impact on sandstone than static dissolution. Both conditions increased porosity in two stages, but dynamic dissolution enhanced pore connectivity while static dissolution caused gradual porosity growth and localized cracks. Dynamic dissolution significantly reduced surface hardness and P-wave velocity, increasing hardness heterogeneity, whereas static dissolution had a milder effect. Additionally, dynamic dissolution notably increased sandstone's hygroscopicity, with moisture absorption rising over time. This study highlights the distinct effects of dynamic and static dissolution on sandstone deterioration, offering insights for the preventive conservation of ancient stone structures. Tailored preservation strategies are essential for addressing these varying degradation mechanisms.  
differential dissolution; pore structure; distribution of mechanical strength; hygroscopic properties +
Footnote †: journal: Applied Sciences
# Surface Reconstruction Planning with High-Quality Satellite Stereo Pairs Searching
## 3 Quality Estimation of Earth Surface Reconstruction
### Regression Model for Quality Estimation
#### 3.2.2 CatBoost Parameter Tuning  
Since there are dependencies among the generated multiple weak learners, the final model can be built as a weighted sum of the regression values from all weak learners. This mechanism makes CatBoost perform well for regression problems that involve multiple features and noisy samples. To integrate multiple base learners, CatBoost employs a serial approach during model tuning. In each tuning round, the sample set remains constant, while the sample weights are continuously updated based on the learned results from the previous round, gradually reducing the bias introduced by noise samples [30].  
The effectiveness of machine learning models is heavily influenced by hyperparameters. To optimize these hyperparameters, we utilize the grid search optimization method (GridSearch-CV) [31]. This approach systematically explores a range of hyperparameter combinations to identify the optimal model parameters by exhaustively searching all possible hyperparameter combinations. For each hyperparameter combination, the model performance is tuned using a five-fold cross-validation to select the best performing set from all attempted parameter combinations.
# Portable Optical Fiber Bragg Grating Sensor for Monitoring Traffic Density
## 3 Experimental Measurement of Transport Vehicles
### Traffic Density Monitoring
#### 3.1.1 Traffic Density on a Suburban Road  
The portable strip was first tested on a street in a built-up area with family houses in the suburban district of Ostrava (Czech Republic) (GPS: 49 deg50'03.3'' N 18 deg07''25.9'' E). Measurements were conducted for 7 h (between 10:00 and 17:00). The sensor measurements recorded the time of passing, type of car (passenger, truck, trailer) and number of axles. All instances of passes were also manually recorded.  
The portable sensor strip was placed on the outer edge of the road, which had a width of 5.125 m (Figure 5). The road was narrowed during the measurements with warning triangles so that cars passing in both directions would be forced to pass over the measuring strip. The reason for narrowing the road during measurement was to obtain relevant information about the total traffic density on the local road.  
Figure 6 shows an example of a car detection response at 50 kph. This particular speed was selected because it represents the legal maximum speed limit in cities in the Czech Republic.  
The dynamic response of the portable sensor is high enough that the individual axles (shown in Figure 6) of the car can be distinguished. The sensor's typical response to a passing car is in the range of 10-1000 pm, depending on the vehicle's passing speed, the point on the portable strip where it passes and its weight.  
According to the manual record, 140 passenger cars passed in the hours from 10:00 to 17:00. The sensor strip detected 139 cars correctly. One passing vehicle was not detected. In another five cases, the sensor incorrectly detected a passing car (false positive). This error was caused by pedestrians and cyclists crossing the sensor. The overall efficiency of detecting cars was 99.286%. The density of operation at hourly intervals is shown in Figure 7.  
Figure 5: Example of measuring station in suburban part of Ostrava.  
Figure 6: The response of a car crossing over a 50 kph speed sensor strip.
# A Posteriori Detection of Numerical Locking in \(hpq\)-Adaptive Finite Element Analysis
## 3 Locking Detection, Assessment, and Resolution
### The Idea and Algorithm of a Posteriori Phenomenon Detection
#### 3.1.1 Solutions from the Equilibrated Residual Method
\begin{array}{cccccc}\
u_{1}&0&0&\
u_{2}&0& \
u_{3}\\ 0&\
u_{2}&0&\
u_{1}&\
u_{3}&0\\ 0&0&\
u_{3}&0&\
u_{2}&\
u_{1}\end{array} \right] \tag{19}\]  
The components of the unit normal vector \(\overset{e}{\boldsymbol{v}}=[\
u_{1},\
u_{2},\
u_{3}]^{T}\) are defined on the element surface \(S_{e}\). The vectorial six-component representations \(\overset{e}{\boldsymbol{\sigma}}\) and \(\overset{f}{\boldsymbol{\sigma}}\) of the stress tensors have to be determined for the element \(e\) and any of its neighbours \(f\). The terms \(\overset{fe}{\boldsymbol{\alpha}}\) and \(\overset{e}{\boldsymbol{r}}\) stand for the splitting function diagonal matrices and stress vectors. These matrices are: \(\overset{fe}{\boldsymbol{\alpha}}=\text{diag}[\alpha_{1},\alpha_{2},\alpha_{3}]\), with \(\overset{fe}{\boldsymbol{\alpha}}=\boldsymbol{1}-\overset{ef}{\boldsymbol{ \alpha}},\boldsymbol{1}=\text{diag}[1,1,1]\) and directional components \(\alpha_{m}\), \(m=1,2,3\). The algorithms for calculation of the splitting functions in the case of the internally unconstrained and constrained (e.g., by the Reissner-Mindlin kinematic constraints) are described in [43; 44; 46], respectively. The alternative is replacement of the equilibrated stresses with their averaged counterparts: \(\overset{fe}{\boldsymbol{\alpha}}=\overset{ef}{\boldsymbol{\alpha}}=\text{ diag}[\frac{1}{2},\frac{1}{2},\frac{1}{2}]\). Comparing relations (15) and (16) one can notice that the minimization of the global energy functional can be replaced with the minimization of local (element) functionals (see [43; 44; 46]). The element functionals read  
\[\overset{e}{\boldsymbol{u}}^{HPQ}\in\overset{e}{\mathcal{V}}^{HPQ}(V_{e}): \overset{e}{B}(\overset{e}{\boldsymbol{u}}^{HPQ},\overset{e}{\boldsymbol{v }})=\overset{e}{L}(\overset{e}{\boldsymbol{v}})+\int_{S_{e}\backslash(P \cup Q)}\overset{e}{\boldsymbol{v}}^{T}\left\langle\overset{e}{\boldsymbol{r }}(\overset{hpq}{\boldsymbol{u}}^{\text{\tiny${\it{1}}$}})\right\rangle\,dS_{e }\,,\ \ \forall\overset{e}{\boldsymbol{v}}\in\overset{e}{\mathcal{V}}^{HPQ}(V_{e}) \tag{20}\]  
where \(\overset{e}{\boldsymbol{u}}^{HPQ}\) is the element solution function and \(\overset{e}{\mathcal{V}}^{HPQ}\) denotes the space of kinematically admissible element displacements \(\overset{e}{\boldsymbol{v}}\equiv\overset{e}{\boldsymbol{v}}^{HPQ}\). As shown in [43; 44], the solution to the local problems (20) exists and is either unique or unique up to rigid body motions, for Dirichlet and Neumann boundary value problems, respectively. The mixed boundary value problems are also possible. Note that one deals with the Dirichlet problems for clamped elements adjacent to the external boundary of the structure, and with the Neumann problems for elements not adjacent to this boundary, namely for elements from the interior of the structure.
## 2 Literature Review
From the analysis of the previous papers, it can be extracted that the application of CNN with time-frequency representations is scarce, with only [15] (STFT) and [11] (PAS)as clear examples with the other papers using a one-dimensional representation of the signal, even if the PAS is more related to the beamform of the signal in space rather than the time series transform. Then, only the STFT is considered in this study. This result is also confirmed by the recent survey on channel identification using ML/DL [11]. On the other hand, different TFTs have been used in combination with CNN in other classification tasks in the wireless communication domain. For example, a comparison of the different TFTs (CWT, spectrogram) for radio frequency fingerprinting (i.e., identification of wireless devices on the basis of their intrinsic physical features) was proposed in [18]. In the field of radio frequency fingerprinting for localization (i.e., the fingerprint is the specific location in the environment), CWT has been used in combination with CNN in [19], where it demonstrated superior performance to another DL method based on the autoencoder, 1D time representation and the shallow machine learning algorithm KNN. In both the applications of CWT described above, the Morlet waveform was used. Then, on the basis of the analysis of the research literature review, we highlight the main contributions of this paper to this field of research. 1. While most of the studies in the literature are focused on the distinction between LOS and NLOS fading conditions, this study evaluates 6 different classes of fading conditions based on 3 GPP TDL fading models where the m parameter of the Nakagami-m fading model is set to different values. 2. The authors have investigated the application of different time-frequency representations (with a specific focus on CWT) in combination with convolutional neural networks to the problem of channel identification with Nakagami-m fading. In comparison to literature where only one mother wavelet is considered, this study evaluates the impact of the choice of the mother wavelet. 3. This paper proposes a novel algorithm for the adaptive selection of the optimal mother wavelet in the CWT in a pre-processing step before the application of the CNN, thus greatly reducing the needed computing time. The results show that the algorithm is able to select the optimal or quasi-optimal mother wavelet, especially in the presence of Gaussian noise.