esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Geological-Tectonic Setting
### Maymundagi Fault
#### 2.1.5. Trench-3 (T-3)  
Moving further east from Trench-2 along the E-W trending Bozkurt segment to the NE-SW trending Dazzkin segment, Trench-3 was located on the southwest flank of this segment. Trench-3 was opened where geological-topographical conditions were suitable, following the southwestern edge of the Maymundagi fault zone, which forms the northern boundary of the Acigol (Denizli-Afyon) graben basin (Figure 17). Morpho-tectonic traces were observed on the eastern side of Acigol, where Maymundagi changes from an east to west direction towards the northeast, meeting Oligocene-aged units and current sediments. At a prominent location where these traces intensified, a trench was excavated along the N 40 W direction, spanning 20 m, with the NE wall being investigated. The coordinates for the NW end are 37.830370\({}^{\circ}\), 29.748604\({}^{\circ}\), and for the SE end are 37.830246\({}^{\circ}\), 29.748782\({}^{\circ}\).  
The trench stratigraphy is dominated by debris slope and alluvial fan lithologies. At the base, a hard-conglomerate sandstone unit outcrops and forms the base block of the fault (Unit A). Overlying this unit is a gravelly sand layer (Unit B), seen intermittently along the trench floor, defining the base level of the trench throughout its length. The fault's hanging wall includes a sparse gravelly clay unit (Unit C), while blocky gravel clay units (Unit D) are visible at various levels within the trench, reaching thicknesses of 10-15 cm. Further up, an open gray clay sand gravel unit (Unit E), covering both sides of the fault and observed along the trench, is present. The uppermost layer comprises natural soil (Unit F) (Figure 18).  
Within Trench-3, at the northern end, a fault plane was observed overlying the conglomerate unit, dipping southeastward. The position of the main fault was measured as N45\({}^{\circ}\) E/50\({}^{\circ}\) SE (Figure 19). The date of the radiocarbon sample taken from the base level of the sparse gravelly clay unit covering the fault (SAH-11) is 10,422 \(\pm\) 37 years. Calendar age is 10,646-10,149 BC. (Figure 20). Detailed information is provided under the title paleoseismological interpretation. This fault trends NE-SW, cutting across the E-W trending Bozkurt segment and indicating that the Dazzkin segment is older than the Bozkurt segment. This is further confirmed by the age data from the base level of the overlying sediment.  
Figure 17: Location and route of Trench-3.  
Figure 19: Fault mirror and lithological levels observed at the NW end of Trench-3.  
Figure 18: View of Trench-3 and the geological cross section on the NE wall of the trench.
## 6 Extension with Side-Lobes/Back-Lobes as Well as the Path Loss Effect
### Antenna Model with Side-Lobes and Back-Lobes
#### 6.1.1 Omni-Directional Antenna  
It is obvious that an omni-directional antenna has antenna gain \(G_{o}=1\) since an omni-directional antenna radiates the power uniformly in all directions, _i.e._, \(U(\theta,\phi)=U_{o}\). Note that we consider the linear gain instead of logarithmic gain (dBi) for an antenna in this paper in order to maintain consistency with the physical channel model (see Section 6.2).
## 3 Results and Discussion
### Analysis of Typical Performances
#### 3.2.4 Space Charge Characteristics
Moreover, because the grain size and amount of the sample were decreased (Section 3.2.1), the interface between the crystal and amorphous regions was decreased. Thus, the charge trap formed by localized state in the interface was decreased [37; 46], which reduced the binding capacity of polyethylene to space charge. Therefore, in the pure polyethylene with modified process, the charge from electrode injection and impurity ionization is more likely to enter the electrode, which leads to the insignificance of the charge peak and the lower accumulation of space charge. Compared with pure polyethylene, the heteropolar space charge near the electrode was increased after the doping microcapsule. There were many small charge peaks, which gradually increased and shifted to the inside of the sample. Furthermore, the mean volume density of space charge in the composite was still at a lower level. A doping microcapsule system can increase the number of polar groups, enhancing the ionization, and thus increasing the accumulation of heteropolar space charge. On the one hand, new charge traps can be introduced by the interface area between the microcapsule and matrix (as shown in Figure 8). On the other hand, the microcapsule increases the number of grains in the polyethylene (Section 3.2.1), so the interface between the crystal and amorphous regions is increased, which increases the number of deep charge traps. Therefore, doping microcapsules can increase the number and depth of charge traps in polyethylene, which enhances the ability of the material to bind to space charge. Because most of the new crystal regions are around the microcapsule, the distribution of charge traps in the composite is related to that of microcapsule (as shown in Figure 1), resulting in more charge peaks. In addition, the composite has a higher binding effect on charge and a higher volume resistance (Section 3.2.2), which not only inhibits the carrier injection, but also hinders the movement of space charge, enhancing the neutralization of ionization products. Thus, the amount of space charge in the composite is low. In conclusion, although doping microcapsules can introduce new impurities to enhance ionization, the number and depth of space charge traps in the polyethylene can be increased by the interface between the microcapsule and matrix. Doping microcapsules can not only inhibit the injection of carriers at the electrode, but also restrict the movement of charge, enhancing its neutralization, thus reducing the space charge in the composite. Therefore, the space charge characteristics of the composite can meet the basic application requirements of polyethylene insulating material.
## 3 Results and Discussion
### Aging Dependency of the Resonance Frequencies of Reduced Ceria
Between a temperature of about \(150\ ^{\circ}\)C and \(270\ ^{\circ}\)C, the resonance frequency curve obtained under rich conditions is slightly above the one measured at \(\lambda=1.02\). This is just mentioned here; it will be discussed later. The very small differences in the resonance frequency between the tests with constant feed gas and the ones with alternating atmospheres can be explained by a slightly different temperature downstream of the catalyst as a result of the different exothermicities of the occurring reactions. One of the key functionalities of the three way catalysts is their oxygen storage capacity to buffer lean-rich variations in the feed gas stoichiometry. By thermal and chemical stress, this oxygen storage capacity decreases with time and has to be monitored in automotive engine exhaust applications (on board diagnosis, OBD). In this study, the OSC was calculated in accordance to ([23], pages 25-28, 140). Thereby, the excess oxygen was integrated starting at a rich-lean change (indicated by the upstream-located wideband lambda-probe \(\lambda>1\)) and ending indicated by a detected lean exhaust downstream of the catalyst (\(U_{\mathrm{Ego}}<450\) mV). As can be seen in Figure 3, the calculated OSC using the data of the lambda probes up- and downstream the catalyst decreases with proceeding aging. The amount of stored oxygen decreases by about \(20\%\) between the fresh state and the first  
Figure 3: Oxygen storage capacity (OSC) during light-off tests with alternating rich-lean feed gases for samples with three different aging states. aging state 1, and by about further 37% between aging state 1 and aging state 2 at a catalyst temperature of 600 \({}^{\circ}\)C. Altogether, a 50% OSC decrease can be noted between the fresh and the second aging state. Since at 600 \({}^{\circ}\)C all resonance frequencies at \(\lambda\) = 1.02 are equal, and since water adsorption can be neglected at this high temperature, it can be assumed that due to the ageing procedure no material loss occurs and therefore, the same amount of ceria is available on the catalyst. Due to aging, the TWC is less reduced under the same reducing conditions, here at \(\lambda\) = 0.98. Therefore, the differences in the resonance frequencies \(\Delta f_{\mathrm{res}}\) measured between \(\lambda\) = 0.98 and 1.02 get increasingly smaller with proceeding catalyst aging since the catalyst gets less reduced with increased OSC loss. The resonance frequency difference at 600 \({}^{\circ}\)C after the first aging step is \(\Delta f_{\mathrm{res}}\) = 38.6 MHz and \(\Delta f_{\mathrm{res}}\) = 22.2 MHz for the second aging, which is a reduction of about 42% between both aging steps. This is in the same range as the OSC deterioration of about 37%.
# Analysis of TDMP Algorithm of LDPC Codes Based on Density Evolution and Gaussian Approximation
## 6 Conclusions  
This paper used the density evolution theory and the Gaussian approximation theory to analyze the TDMP algorithm. The TDMP algorithm optimizes information transmission in the decoding process, that is, the update of the current layer information directly uses the updated information of the previous layer, thereby accelerating the convergence speed of the decoding. Based on the density evolution analysis of the existing BP algorithm, this paper analyzed the TDMP algorithm from the perspective of density evolution, giving the theoretical analysis process of the TDMP algorithm, and verifying the analysis results of the TDMP algorithm through simulation comparison with the BP algorithm in this paper. In addition, a simplified TDMP algorithm based on a normalized factor was proposed. The simulation results show that the proposed optimization algorithm had similar performance with the TDMP algorithm. Moreover, it did not involve the operation of non-linear functions and was easier to implement in the hardware. We further simulated six code rates of LDPC codes under the IEEE 802.16e standard and obtained the performance curves of the BP algorithm and the TDMP algorithm based on density evolution and Gaussian approximation, respectively. Thus, we can prove that the decoding convergence speed of the TDMP algorithm was faster than the BP algorithm; meanwhile average iterations were fewer from the perspective of density evolution theory and Gaussian approximation, respectively. According to the structure of LDPC codes under the known standard and the simulation results in the text, we can conclude that the density evolution and Gaussian approximation of the proposed TDMP algorithm can be applied to different standards.  
[PERSON] and [PERSON] initiated the idea, designed the experiments, analyzed the data, and wrote the manuscript. [PERSON] and [PERSON] helped to polish the idea and the analysis. [PERSON] and [PERSON] made the revisions for the paper. All authors have read and approved the final manuscript.  
This research was funded by the National Natural Science Foundation of China, under Grant No. 51874264, 61571108, and 61501158.  
The authors declare no conflict of interest.
# Deep Learning Methods for Space Situational Awareness
## Abbreviations  
\begin{tabular}{l l}
**Acronym** & **Explanation** \\ A3C & Actor-Critical Method \\ AI & Artificial Intelligence \\ AIOT & Artificial Intelligence of Things \\ AMIGO & Adaptive Markov Inference Game Optimization \\ ANN & Artificial Neural Network \\ AOT & Active Object Tracking \\ ASPP & A parallel Spatial Pyramid Pooling \\ CCTV & Close-Circuit Television \\ CNN & Convolutional Neural Network \\ DL & Deep Learning \\ DNN & Deep Neural Network \\ DRL & Deep Reinforcement Learning \\ FC & Fully Connected \\ FCC & Federal Communications Commission \\ FCN & Fully Convolutional Network \\ FFSM & Floating Space Manipulators \\ FFT & Fast Fourier Transform \\ FGBNN & Fast Grid-Based Neural Network \\ FNN & Fuzzy Neural Network \\ FOV & Field of View \\ ILSVRC & ImageNet Large Scale Visual Recognition Challenge \\ IOT & Internet of Things \\ ISS & International Space Station \\ K-NN & k-Nearest Neighbor \\ K-NN-DTW & k-Nearest Neighbor combined with Dynamic Time Warping \\ LEO & Low Earth Orbit \\ LSTM & Long-Short Term Memory \\ MCNNT & Modified Convolutional Neural Networks Technique \\ MEO & Medium Earth Orbit \\ MF-TBD & Multi-Frame Track-Before-Detect \\ ML & Machine Learning \\ PPO & Proximal Policy Optimization \\ PRF & Pulse Repetition Frequency \\ RCS & Radar Cross Section \\ RELU & Rectified Linear Unit \\ RL & Reinforcement Learning \\ RNN & Recurrent Neural Network \\ ROI & Region of Interest \\ SDA & Space Domain Awareness \\ SGDM & Stochastic Gradient Descent with Momentum \\ SIOT & Satellite Internet of Things \\ SM & Sensor Management \\ SNR & Signal to Noise Ratio \\ SSA & Space Situational Awareness \\ STM & Space Traffic Management \\ SVM & Support Vector Machine \\ TDNN & Time-Delay Neural Network \\ UAV & Unmanned Aerial Vehicles \\ \end{tabular}
## 2 Theory
### Dynamical Mean-Field Theory for Electromagnetically Driven Semiconductors in d = 3 Dimensions  
The Hamiltonian for the correlated system, \(U\,\
eq\,0\), Equation (1), is solved numerically with a single-site Dynamical Mean Field Theory (DMFT) [33; 34; 35] and an iterated perturbation theory solver (IPT), see Figure 2f. The laser-band-electron Green's function \(G_{\text{LB}}^{R}(k,\omega)\), Equation (4), is characterized by the wave vector \(k\), describing the periodicity of the lattice, the electronic energy \(\omega\) and the external driving frequency \(\Omega\) as contained in the Floquet indices \((m,n)\). The DMFT self-consistency relation assumes the form of a \(2\times 2\) dimensional matrix equation in regular Keldysh space and in becomes \(n\times n\) dimensional in Floquet space. The IPT is generalized to Keldysh-Floquet form as well. The resulting numerical algorithm proves to be efficient and stable for all values of \(U\).  
The coupling \(\vec{d}\cdot\vec{E}_{0}\cos(\Omega_{L}\tau)\) under the assumption of the Coulomb gauge \(\vec{E}(\tau)\,=\,-\frac{\partial}{\partial\tau}\vec{A}(\tau)\), that is in Fourier space \(\vec{E}(\Omega_{L})\,=\,i\Omega_{L}\cdot\vec{A}(\Omega_{L})\), generates the factor \(\Omega_{L}\) that cancels the \(1/\Omega_{L}\) term in the renormalized cylindrical Bessel function; for details see Equation (7) of Ref. [34]. By checking the Floquet sum we find that considering the first ten (\(n=10\)) Floquet modes is sufficient within the numerical accuracy of the DMFT [14; 15]. A cut-off after a smaller number would lead to a drift in the total energy of the system and thus it would hurt conservation laws.  
While the quasienergy spectra of excitons and the Franz-Keldysh effect in the non-equilibrium have been broadly investigated in d = 2 dimensions for semiconductors in the THz regime, especially for GaAs [42; 43; 44]; only recently the first numerical studies of the excitonic quasienergy spectra under topological excitations in the optical regime have been performed for ZnO with DMFT in d = 3 dimensions [14; 15]. Whereas DMFT has been so far preferentially used for typical Mott insulators [45] like NiO [46; 47], this DMFT study [14; 15] is remarkable, since ZnO as a wide-gapped transition metal oxide from the group IIb in the table of the elements which may be considered to behave as a Mott insulator at zero temperature [16; 18], especially in driven nano-structures this is the case. ZnO is broadly investigated for random lasing in disordered ensembles of nano-pillars and nano-grains, i.e. Mie spheres, and a restriction to two-dimensional electronic processes is no necessary condition in these ensembles [10]. Two-dimensionality is _a priori_ also not necessary for the considering the formation of polaritons [1].
## 3 Materials and Methods
### Evaluation Metrics  
In this study, we used several evaluation metrics to measure the model's performance in the remote-sensing image segmentation task. Due to the class imbalance and large background regions in remote-sensing images, these metrics provide a comprehensive reflection of the model's performance in handling large-scale, multi-class semantic segmentation tasks. We used the following main evaluation metrics: accuracy, recall, F1-score, IoU, and mPA. Accuracy represents the proportion of correctly predicted positive instances among all positive predictions. This is a key metric when minimizing false positives is important, as it highlights the model's ability to avoid misclassifying food areas as non-food areas (see Formula (16)).  
\[\frac{\text{Precision}=\mathit{TP}}{(\mathit{TP}+\mathit{FP})}\frac{\text{ Recall}=\mathit{TP}}{(\mathit{TP}+\mathit{FN})}\frac{\text{F1 score}=(2 \times\text{Precision}\times\text{Recall})}{(\text{Precision}+\text{Recall})} \tag{16}\]  
where \(\mathit{TP}\) represents the number of pixels correctly predicted as non-food crops, \(\mathit{FN}\) represents the number of non-food crop pixels misclassified as background pixels, and \(\mathit{FP}\) represents the number of background pixels incorrectly predicted as non-food crops.  
Additionally, more comprehensive evaluation metrics include Intersection over Union (IoU) and mean Pixel Accuracy (mPA) mPA is the simple average of per-class pixel accura  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Object Pixels Ratio**} \\ \hline
**Dataset** & \
\begin{tabular}{c} **HN-NG Set Object Pixels** \\ **(Proportion)** \\ \end{tabular} &
\begin{tabular}{c} **CCD Set Object Pixels** \\ **(Proportion)** \\ \end{tabular} \\ \hline Training & 46,355,337 (8.21\%) & 5,571,000 (4.78\%) \\ Validation & 15,451,779 (2.74\%) & 1,194,480 (1.02\%) \\ Test & 15,451,779 (2.74\%) & 1,202,040 (1.07\%) \\ Total & 77,258,895 (13.69\%) & 7,967,520 (6.83\%) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical data of non-food crop classes and their proportion of the total image pixels in the dataset.  
cies. Because every class contributes equally to the average, mPA highlights performance on minority classes and prevents the majority class from dominating the score, making it a useful complement to IoU and F1 in imbalanced crop-mapping tasks. IoU refers to the proportion of correctly predicted pixels in the union of predicted and actual pixels, while mPA calculates the proportion of correctly classified pixels for each class (see Formula (17)).  
\[\frac{\text{IoU}=\text{TP}}{(\text{TP}+\text{FP}+\text{FN})}mPA=\frac{1}{K+1} \sum_{t=0}^{k}\frac{TP}{TP+FP} \tag{17}\]  
The current task involves four classes: rapeseed, tea, cotton, and background. Through the comprehensive analysis of these evaluation metrics, we are able to assess the model's multi-class semantic segmentation capability in large-scale regional images and complex environments, providing data support for further model optimization and helping us achieve better performance in practical applications.
## 3 Results and Discussion  
### Relationships between NDVI and Vegetation Parameters  
Figure 5a shows the relationship between the LAI estimated from ground measurements and the NDVI computed from different optical images. A classical logarithmic relationship is clearly observed, in accordance with similar results observed by [PERSON] _et al._[5] and [PERSON] _et al._[13], and is described by the following equation:  
\[LAI=\frac{1}{k_{LAI}}\ln\left[\frac{NDVI-NDVI_{\mathrm{max}}}{NDVI_{\mathrm{ min}}-NDVI_{\mathrm{max}}}\right] \tag{4}\]  
where NDVI\({}_{\mathrm{max}}\) is the NDVI value for a maximum value of LAI (=0.89), NDVI\({}_{\mathrm{min}}\) is the bare soil NDVI value (=0.22), and k\({}_{\mathrm{LAI}}\) is the extinction coefficient (=0.69). The coefficient of determination R\({}^{2}\) that was obtained is 0.63, with a RMSE (relative mean square error) of 0.72 m\({}^{2}\)/m\({}^{2}\).  
[PERSON] _et al._[15] found a k\({}_{\mathrm{LAI}}\) of 0.71 from Formosat-2 images acquired on a larger area in the same region, including wheat, rice and irrigated grassland. For wheat plots on the Kairouan plain (Tunisia), [PERSON] _et al._[46] found a k\({}_{\mathrm{LAI}}\) of 1.24.  
Figure 4: Temporal evolution of vegetation parameters for the sampled plot 1a. Vertical lines indicate cut dates.  
The correlations between the NDVI and the vegetation parameters HVE, BIO and VWC are displayed in Figure 5b-d. For these graphs, a linear relationship is observed between NDVI and the vegetation variables when the NDVI is less than 0.8. Above this, the NDVI saturates and does not vary with increases in the different vegetation parameters (the threshold value for the estimation of HVE from the NDVI is 30 cm, and the threshold is approximately 1 kg/m\({}^{2}\) for BIO and VWC).
## 3 Numerical Experiments and Analysis
### CC-SHADE-ML
In the original paper [16], the authors also found that MLCC demonstrates better results with the power value (3) equal to 7. In Figure 4, we show an example of curves which demonstrate the dynamic adaptation of the number of subcomponents and the population size in one independent run of CC \begin{table}
\begin{tabular}{c c c c c c c} \hline
**CC vs.** & **CC-k(1)** & **CC-k(2)** & **CC-k(3)** & **CC-k(5)** & **CC-k(7)** & **CC-k(10)** \\ \hline + & 215 & 215 & 193 & 218 & 185 & 190 \\ - & 417 & 421 & 439 & 423 & 448 & 452 \\ \(\approx\) & 118 & 114 & 118 & 109 & 117 & 108 \\ \hline \end{tabular}
\end{table} 
Table 2: The Wilcox rank-sum test CC vs. CC-k(v). Figure 3: The ranking of CC-k algorithms. k(7). The \(x\)-axis denotes the FEs, the \(y\)-axis denotes the selected level of parameters. The pictures show graphs for \(F_{1}\), \(F_{2}\), \(F_{12}\), and \(F_{15}\) benchmark problems. Figure 5 shows convergence graphs for CC-k(v) algorithms. The \(x\)-axis denotes the FEs number, the \(y\)-axis denotes the averaged fitness value obtained in 25 independent runs. As we can see, the convergence plots are almost similar for all CC-k algorithms. In most cases, the value of the power in (3) does not critically affect the behavior of the CC-SHADE-ML algorithm. As we have noted, according to the results from Table 4, the algorithms' performance is the same on the majority of problems. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number** & **Algorithm** & **Total Win** & **Total Loss** & **Total Equal** & **Averaged Rank** \\ \hline (5) & CC-k(7) & 10 & 1 & 64 & 4.47 \\ (6) & CC-k(10) & 10 & 5 & 60 & 3.87 \\ (3) & CC-k(3) & 6 & 4 & 65 & 3.87 \\ (4) & CC-k(5) & 7 & 1 & 67 & 3.13 \\ (2) & CC-k(2) & 4 & 9 & 62 & 2.67 \\ (1) & CC-k(1) & 1 & 18 & 56 & 3.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The sum of the Wilcoxon test results. Figure 4: The self-adaptation curves for CC_size and pop_size of CC-k(7) in an independent run on: (a,c,e,g)—CC-size for \(F_{1}\), \(F_{2}\), \(F_{12}\), \(F_{15}\) and (b,d,f,h)—pop-size for \(F_{1}\), \(F_{2}\), \(F_{12}\), \(F_{15}\). Figure 5: Convergence plots of CC-k algorithms. Benchmark problems are: (**a**) \(F_{1}\); (**b**) \(F_{2}\); (**c**) \(F_{3}\); (**d**) \(F_{4^{\prime}}\); (**e**) \(F_{5^{\prime}}\); (**f**) \(F_{6^{\prime}}\); (**g**) \(F_{7^{\prime}}\); (**h**) \(F_{8}\); (**i**) \(F_{9^{\prime}}\); (**j**) \(F_{10^{\prime}}\); (**k**) \(F_{11^{\prime}}\); (**d**) \(F_{12^{\prime}}\); (**m**) \(F_{13^{\prime}}\); (**n**) \(F_{14^{\prime}}\); (**o**) \(F_{15}\).