esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 PIESAT-01 Multiple Satellites Interferometric Imaging
### High Fidelity Phase Imaging  
To address the reduced coherence between main and auxiliary images and the interference phase error caused by Doppler spectrum shift along the along-track baseline, a pre-imaging procedure is implemented. Initially, the overlapping Doppler spectra are identified based on the center frequency and Doppler bandwidth of the main and auxiliary satellite echo data. Subsequently, the non-overlapping portion of the Doppler spectrum is filtered out prior to imaging.  
Unlike monostatic SAR, the slant range trajectory between the bistatic strip SAR satellite and the target follows a bi-square root form, which can be expressed using Taylor expansion as follows:  
\[\begin{split}& R(t_{a};R_{0})=\frac{1}{2}\Big{(}\sqrt{R_{1}^{2}+ \big{[}v_{1}(t_{a}-t_{p})\big{]}^{2}-2R_{1}v_{1}(t_{a}-t_{p})\sin\phi_{1}}+ \sqrt{R_{2}^{2}+\big{[}v_{2}(t_{a}-t_{p})\big{]}^{2}-2R_{2}v_{2}(t_{a}-t_{p}) \sin\phi_{2}}\Big{)}\\ &\approx R_{0}+k_{2}(t_{a}-t_{p})^{2}+k_{3}(t_{a}-t_{p})^{3}+k_{4 }(t_{a}-t_{p})^{4}+\ldots\end{split} \tag{5}\]  
Furthermore, the bistatic strip SAR signal model can be represented as follows:  
\[s(t_{r},t_{a})=\sigma a_{r}(t_{r}-2R(t_{a})/c)\cdot a_{a}(t_{a})\exp\Big{[} \mathrm{j}\pi K_{r}(t_{r}-2R(t_{a})/c)^{2}\Big{]}\cdot\exp(-\mathrm{j}\pi R(t _{a})/\lambda) \tag{6}\]  
where \(\sigma\) represents the target's backscattering coefficient, \(c\) denotes the speed of light, \(\lambda\) signifies the signal wavelength, \(\alpha_{r}(\cdot)\) and \(\alpha_{a}(\cdot)\) denote the temporal distance envelope and azimuth envelope of the signal, respectively, \(t_{r}\) represents the range-time, and \(K_{r}\) denotes the signal frequency modulation.  
Perform azimuth Fourier transform on the received signal, followed by multiplication with the following variable standard equation:  
\[s_{sc}(t_{r})=\exp\Big{[}\mathrm{j}C_{2}\Big{(}t_{r}-\tau_{ref}(f_{a})\Big{)} ^{2}\Big{]} \tag{7}\]  
The purpose of this step is to correct residual range cell migration (RCM), followed by performing range Fourier transform and subsequently conducting range pulse compression and range migration correction, namely, multiplication with  
\[H(f_{r},f_{a})=\exp\Big{[}\mathrm{j}\frac{\pi^{2}}{-\pi K_{s}(f_{a})+C_{2}(f_ {a})}f_{r}^{2}+\mathrm{j}2\pi f_{r}\Big{(}\tau_{ref}(f_{a})-\tau_{ref}(f_{ \mathrm{dc}})\Big{)}\Big{]} \tag{8}\]  
After performing the inverse range Fourier transform, azimuth compression is then conducted, with the azimuth-matched filter being applied:  
\[H(R_{0},f_{a})=\exp\Bigg{[}-\mathrm{j}\pi\frac{\lambda}{4k_{2}}f_{a}^{2}- \mathrm{j}\pi\frac{\lambda^{2}k_{3}}{16k_{2}^{3}}f_{a}^{3}-\mathrm{j}\pi\frac {\lambda^{3}(9k_{3}^{2}-4k_{2}k_{4})}{256k_{2}^{5}}f_{a}^{4}\Bigg{]} \tag{9}\]  
Upon performing the inverse azimuth Fourier transform, the focused 2D SAR image is obtained.
## 4 Simulation Results and Analysis
### Solidification Process
#### 4.1.3 Effect of Seawater Temperature on Solidification Process  
The temperature of seawater gradually decreases with depth in the vertical direction. It is about 26-27 \({}^{\circ}\)C in the surface layer (sea surface to 60 m deep), 60-300 m is a variable temperature layer, and the temperature does not change much, but below 300 m. The temperature drops to about 4 \({}^{\circ}\)C [1].  
Figures 2-4 are calculated according to the seawater temperature of 10 \({}^{\circ}\)C. In order to analyze the influence of seawater temperature on the solidification process, this paper also simulates and analyzes the seawater temperature of 7 \({}^{\circ}\)C. The cylindrical phase change heat exchanger with an outer diameter of 0.26 m and a height of 0.95 m was used in the analysis.  
By comparing Figures 3 and 5, it can be clearly seen that as the seawater temperature decreases, the total liquid-phase ratio of the phase change material decreases significantly, and the solidification rate increases significantly. The numerical results also show that the time required to complete the phase change process of the cylindrical phase change heat exchanger with a seawater temperature of 7 \({}^{\circ}\)C, an outer diameter of 0.26 m, and a height of 0.95 m is only 3470 s, which is 1330 s shorter than that of the seawater temperature of 10 \({}^{\circ}\)C.
## 3 System Model
### Input-Output Equations  
The data from node S is transmitted via direct and a relay path.  
**Time Slot 1:**  
The received data equation at node \(D\) for time slot 1 is shown as in Equation (1) [8].  
\[Y_{SD}=h_{SD}\sqrt{P_{SD}}x+n_{SD} \tag{1}\]  
where \(h_{SD}\) is the channel response between is source and destination, \(P_{SD}\) is the transmit signal power from source to destination, \(x\) is the signal transmitted, and \(n_{SD}\) is the channel noise between \(S\) node to \(D\) node. The signal that is received at the node R in first time slot is shown by:  
\[Y_{SR}=h_{SR}\sqrt{P_{SR}}x+n_{SR} \tag{2}\]  
where \(h_{SR}\) is channel response between source and relay are, \(P_{SR}\) is the transmit signal power from source to relay, \(x\) is the transmitted signal, and \(n_{SR}\) is the channel noise between S node to R node.  
**Time Slot 2:**  
In second time slot, the relay receives the signal from S node, amplifies it, and retransmits it to the D node. The received signal through the R node in time slot 2 is given as:  
\[Y_{RD}=h_{RD}\sqrt{P_{RD}}\;Y_{SR}+n_{RD} \tag{3}\]  
where channel response between relay and destination is \(h_{RD}\), \(P_{RD}\) is the signal power, \(Y_{SR}\) is the transmitted signal, and \(n_{RD}\) is the channel noise between the S node to R node. Signal from the S and R node is combined using \(MRC\) at D node. The MRC at the D node is given as:  
\[Y_{MRC}=Y_{SD}+Y_{RD} \tag{4}\]
# Classification of Road Surfaces Based on CNN Architecture and Tire Acoustical Signals
## 2 Background Theory
### Tire-Pavement Interaction Noise  
Tire-pavement interaction noise refers to the noise generated by the interaction between wheels and road surfaces while driving. There are several types of sources; for example, road induced, tire induced, road surface induced, and environment induced. Figure 4 shows the relative contributions of various influencing factors to the overall noise level of the TPIN [19]. Because there are too many details for each factor, only three dominant factors, speed, road, and tire, are considered within the required scope.  
Speed plays a more important role than other factors. This means that the noise level of the TPIN is strongly affected by speed [19; 20]. There are two main reasons for this finding. First, the first is the tread impact noise generated by physical contact between the tire and the road surface. The impulse level between the tire and road is proportional to the rotation speed of the tire, which is directly related to the car speed. The second is air pumping noise. The main generation mechanism of air pumping noise is the compression expansion action of air. The inflow and outflow of air into the tire groove and tread increase as the speed increases [21]. Such volume fluctuations directly induce pressure fluctuations, that is sound waves. These noises usually have a frequency range of 1000-2500 Hz. The experimental relationship between speed and TPIN is shown in Equation (7). Here, \(L\) is the acoustic pressure level, and \(v\) is the vehicle speed. \(A\) and \(B\) are coefficients that depend on environmental factors.  
\[L=A+B\;log(v) \tag{7}\]  
There are many other secondary effects in addition to those mentioned above. The horn effect, Helmholtz resonance, sidewall resonance, torus cavity resonance, and tire-rim assembly resonance are good examples [19; 20; 22]. In the road part, the roughness of the pavement texture, that is, the road texture, is worth covering. The roughness of the pavement texture indicates the extent of deviation of the surface profile from the reference plane. Pavement textures can be divided into several categories based on the texture wavelength. The microtexture had a wavelength smaller than 0.5 mm. The macro-texture's wavelength is between 0.5 and 50 mm. The mega-texture has a wavelength range of 50-500 mm. Mega-textures are known to be the main source of noise generation.
## 3 Methodology and Models
### Feature Engineering
#### 3.2.2 Sequence Padding  
To handle the time-series nature of the data, the processed features were organized into sequences of 10 time-lag steps, allowing the model to learn temporal dependencies within the data. Different time-step lengths were examined, evaluating the model's performance using various metrics such as MAE, RMSE, and MAPE. The results, as shown in the table below, indicate that 10 time-lag steps offered the best balance between predictive accuracy and computational efficiency, with no significant performance gains observed with longer sequences. The data were reshaped into 3D tensors, where each sample consisted of a sequence of time steps. In Table 1 the results of the timed step examination for the optimal selection of the time step number are presented.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Time-Lag Steps** & **MAE (MWh)** & **RMSE (MWh)** & **MAPE (\%)** \\ \hline
5 & 95.12 & 110.24 & 19.2 \\
10 & 88.72 & 102.09 & 17.5 \\
15 & 89.45 & 104.58 & 18.0 \\
20 & 90.15 & 106.12 & 18.3 \\ \hline \end{tabular}
\end{table}
Table 1: Sequence padding examination.  
Figure 4: Histogram for the output feature.
# Measurement of Diurnal Variation in Needle PRI and Shoot Photosynthesis in a Boreal Forest
## 1 Introduction
In a boreal forest, canopy structure does not change on a daily time scale, but illumination geometry and the spectral composition of incident radiation (caused by the varying fraction of diffuse sky radiation dominated by shorter wavelengths) does vary with the solar angle continuously on a clear day. These non-physiological variations need to be removed before canopy PRI measurements made at different times of day can be compared. Measurements of leaf PRI daily curves are, therefore, necessary tovalidate the algorithms for removing the non-physiological PRI components. Currently, information on the daily cycle of leaf-level PRI in natural environments is limited. In the present study, we provide the first account of the diurnal cycle of needle PRI in a boreal Scots pine stand, under natural illumination, and its relationship with primary productivity. The aim of the research was to assess the potential of instantaneous measurements of canopy PRI for monitoring the photosynthesis of a boreal conifer forest.
## 5 Comparison and Discussion Between KANs and MLPs
### Prediction Errors and Discussion
#### 5.2.2 KAN-B vs. MLP-B  
Compared to KAN-A and MLP-A, both KAN-B and MLP-B feature streamlined intermediate layers (only 3 nodes), yet structural simplification exerted divergent impacts on model capabilities.  
Although KAN-B reduced hidden nodes from 10 (KAN-A) to 3, adaptive adjustments of learnable B-spline basis functions preserved the fine-grained characterization of univariate functions, theoretically maintaining strict function approximation. The experimental results demonstrated R\({}^{2}\) values of 0.9990 (training) and 0.9988 (test). This confirms that there is no loss of theoretical completeness due to structural compression.  
MLP-B suffered from dimensional collapse. Reducing the hidden nodes from 10 to 3 shrank the parameter space from (3 \(\times\) 10 + 10 \(\times\) 1) = 40 to (3 \(\times\) 3 + 3 \(\times\) 1) = 12. This led to deteriorated error metrics. The average training R\({}^{2}\) plummeded to 0.6644, a 28% drop from MLP-A, indicating ineffective nonlinear mapping caused by excessive simplification.  
MLP-B's average R\({}^{2}\) and Adjusted R\({}^{2}\) on both datasets fell below 0.7, losing predictive validity. This validated the limitations of [PERSON]'s theorem: fixed activation functions with insufficient hidden nodes cannot approximate complex functions via linear combinations.  
In contrast, KAN-B's performance remained stable. The adjusted R\({}^{2}\) values remained near perfection at 0.9990 for training and 0.9988 for testing. The exceptional performance was attributed to the local segmental property of spline basis functions. Their adjustable coefficients effectively compensated for expressive loss caused by the node reduction.  
The runtime trends diverged sharply. KAN-B achieved optimization with 299.53 s, a 50% reduction from KAN-A's 614.51 s. MLP-B's average runtime increased to 7.12 s, likely due to the optimization of oscillations and amplified gradient noise from insufficient parameters.  
These findings highlight a fundamental distinction: MLPs rely on fixed activation function superpositions (governed by the universal approximation theorem), with expressivity constrained by depth and width. KANs achieve efficient nonlinear transformations through learnable spline functions. These functions are theoretically grounded in KART, which proves that any multivariate continuous function can be decomposed into finite univariate combinations. The mathematical foundation endows KANs with theoretically exact approximation capabilities.
# Strength Prediction Model for Cohesive Soil-Rock Mixture with Rock Content
## 3 Analysis of Large-Scale Triaxial Compression Test Results of Cohesive SRM
### Shear Strength of Cohesive SRM  
Figure 8 depicts the strength curves of cohesive SRM samples under different confining pressures and P\({}_{\rm BV}\) values. The results indicated that, under constant confining pressure, the shear strength of the samples increased with increasing P\({}_{\rm BV}\). At lower P\({}_{\rm BV}\) values (20% \(<\) P\({}_{\rm BV}\)\(<\) 40%), the growth of the curves was moderate; however, at higher P\({}_{\rm BV}\) values (40% \(<\) P\({}_{\rm BV}\)\(<\) 60%), a significant increase in the curves was observed. This phenomenon was attributed to the fact that, at lower P\({}_{\rm BV}\) values, the SRM was predominantly composed of cohesive soil with a limited number of rock blocks. In this case, the mixture structure was looser, and the rock blocks remained dispersed within the cohesive soil, thus failing to form an effective skeleton structure. The mechanical properties of cohesive soils play an important role at this stage. However, the shear strength of cohesive soil was relatively low and varied more gently. Therefore, at lower P\({}_{\rm BV}\) values, adding small amounts of rock blocks did not significantly enhance the overall shear strength, resulting in a flatter increase in the curve. As P\({}_{\rm BV}\) increased, the number of rock blocks also increased, gradually forming a skeleton structure. This skeleton structure effectively bore the external loads and improved the overall stability of the mixture. The increased mutual contact and occlusion between the rock blocks allowed the SRM to better transfer stresses when subjected to shear forces, thereby increasing the shear strength. As a result, the growth of the curve became significantly pronounced.  
Figure 7: Stress-strain curves for samples with different P\({}_{\rm BV}\) values.
## 1 Introduction  
Motivated by the limited knowledge concerning the M > 6 earthquake of 1654, this study is an attempt to understand the event that damaged the region of Lazio-Abruzzo in Central Italy, less than 100 km from Rome and Naples (Figure 1). Records on this earthquake are available but they are too old for seismogram data and are beyond the age limit for applying seismological analysis to robust historical documentation, including recognition of the causative fault that ruptured during the event. For these reasons, the approach used could not be solely based on direct data so validation through modeling was used as well.  
The study area is located in the Central Apennines, an East verging, fold-and-thrust belt that developed during the Late Cretaceous to present Africa-Europe plate convergence [1, 2]). The present-day landscape and tectonic setting of the region is the result of a long deformation history, characterized by cyclical extensional and contractional phases [3]. The regional seismicity and fault setting reflects the present-day NE-SW-oriented extensional regime characterizing the Central Apennines [4], with a broad and complex system of normal faults that dissect the belt and crosscut the pre-existing compressional structures.  
Our analysis is based on the different typologies of direct historical cooseismic data and geological/seismological data. Among the historical data, we utilize records extracted from the available seismic catalogues [5] and the hydrological and geological earthquake signatures newly acquired and derived from the archival research conducted in this study. Among the geological data, we take into account the active faults commonly considered as potential sources of the 1654 event. With regard to the seismological data, the distribution and the parameters of the present-day instrumental seismicity are considered.  
The 1654 earthquake had an MCS intensity of 9-10 and an Me of 6.33+/ \(-\)0.14 [5], with the uncertainty in its epicentral location being +/ \(-\)2.5 km. Although which fault caused this earthquake is still unknown, the 1654 earthquake occurred in a highly seismogenic region in Central Italy, which has been struck by medium to large earthquakes in present and historical times [5], with a \(\sim\)200-year M6+ earthquakes average regional recurrence time [6]. The main aim of this study is to identify the source of the 1654 earthquake and to provide suggestions regarding the kinematics of the 1654 rupture mechanism. To this aim, we (1) modeled the intensity data (Boxer 4.0 code by [PERSON] et al. [7], deriving different types of macroseismic sources; (2) calculated the strain fields imposed by all the potential faults; (3) analyzed which sources best matched theoseismic hydrological/geological signals that we collected; and (4) discriminated among the resulting potential faults considering the seismological imprint of the region as defined by the instrumental seismicity and by the geometry of the active faults in the area.
# High-Precision Single-Photon Laser Time Transfer with Temperature Drift Post-Compensation
## 5 Conclusions  
In this paper, we established a laser time transfer experiment setup to test the system propagation delay in a room temperature tuning range from 22 \({}^{\circ}\)C to 34 \({}^{\circ}\)C and proposed a temperature drift post-compensation method to compensate the system propagation delay. As a result, the original system propagation delay drift of 240 ps caused by temperature change was reduced by compensation of more than two orders of magnitude. The temperature drift coefficient of detection delay was decreased from the original value of 20 ps/\({}^{\circ}\)C down to \(-\)0.05 ps/\({}^{\circ}\)C. The time deviation of detection delay in the temperature stable period was improved from the original value of 16 ps down to 1.2 ps over 40,000 s. In the complete experiment, the time deviation was reduced down to 2.5 ps from the original value of 11.5 ps over 80,000 s. Thus, the post-compensation method used in this experiment performed a significant compensation for the system delay drift. The corresponding TDEV long-term stability was increased by a 0.5-1 order of magnitude. With a compact design and other methods such as thermal isolation, this post-compensation method will help to establish an ultra-stable laser time transfer system for space application.  
Conceptualization, W.M., G.W. and I.P.; methodology, W.M. and Y.W.; software, W.M. and Y.W.; validation, G.W. and S.J.; formal analysis, W.M.; investigation, Y.W.; resources, Z.Z. ([PERSON]); data curation, K.T. and Z.Z. ([PERSON]); writing--original draft preparation, Y.W. and W.M.; writing--review and editing, W.M., Z.Z. ([PERSON]), I.P. and S.J.; visualization, Z.Z. ([PERSON]); supervision, S.J.; project administration, G.W. and Z.Z. (Zhongping Zhang); funding acquisition, Z.Z. ([PERSON]), W.M. and G.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 11774095, 11804099 and U1231107, the Youth Innovation Promotion Association of CAS, id. 2018303, and the Ministry of Education of the Czech Republic, grant number RVO68407700 and 8H17019.  
The authors declare no conflict of interest.
# Sleep Apnea Classification Using the Mean Euler-Poincare Characteristic and AI Techniques
## 6 Conclusions and Future Works  
This study introduced a novel method for detecting obstructive sleep apnea (OSA) using single-lead ECG signals by leveraging the Euler-Poincare characteristic (EPC) to represent ECG peaks geometrically. The RAP-based methodology for smoothing the ECG signal and extracting EPC provided a rich feature set that captured the intricate patterns of ECG peaks. We then applied an ensemble K-nearest neighbors (EKNN) classifier to these EPC features, outperforming traditional methods by combining multiple KNN models for improved accuracy. This approach highlights the effectiveness of using geometric representations and advanced ensemble techniques to enhance OSA detection from ECG data.  
In conclusion, using the EKNN as a classifier during the training phase, we achieved an accuracy of 80%, with a sensitivity of 82% and a specificity of 71%. In the test phase, we attained an accuracy of 97%, a sensitivity of 96%, and a specificity of 100%.  
For the FNN, as a binary classifier, we recorded a perfect accuracy of 100%, with a sensitivity of 96% and a specificity of 100% in the training phase. In the test phase, we achieved an accuracy of 97%, a sensitivity of 93%, and a specificity of 100%.  
In future works, the NGRF will be built using smooth signals from the P-waves, QRS-peaks, and T-waves to obtain a model that represents the behavior of the full ECG instead of only one section (P-R section).  
Our framework works with the EPC as a direct input to the classifier. In future works, we will include different features in various domains (time, frequency, geometric, and statistical) and larger datasets to classify various diseases.  
Conceptualization, M.R.-M. and G.O.-T.; methodology, F.D.J.S.-V. and M.M.G.; validation, E.S.-B. and J.C.M.-S.; formal analysis, J.E.V.-R. and E.M.R.-V.; Investigation, M.R.-M. and M.G.M.-E.; writing--review and editing, J.Y.R.-M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The database Apnea-ECG, described in [42], is available on PhysioNet at [[https://physionet.org/content/apnea-ecg/1.0.0/](https://physionet.org/content/apnea-ecg/1.0.0/)]([https://physionet.org/content/apnea-ecg/1.0.0/](https://physionet.org/content/apnea-ecg/1.0.0/)) (accessed on 22 August 2024).  
We thank the Centro Universitario de los Valles of the University of Guadalajara for the access and use of the research laboratories.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} Accu & accuracy \\ BMI & body mass index \\ CNN & convolutional neural network \\ CTM & central tendency measure \\ DWT & discrete wavelet transform \\ ECG & electrocardiogram \\ EDR & ECG-derived respiration \\ EEG & electroencephalogram \\ EKNN & ensemble k-nearest neighbors \\ EMG & electromyogram \\ EPC & Euler-Poincare characteristic \\ FENet & frequency extraction network \\ FNN & feedforward neural network \\ \end{tabular}