esa-sceva/satcom-chunk-collection huggingface dataset:
# Astronomical Investigation to Verify the Calendar Theory of the Nasca Lines
## 2 The Sun Worship in Pre-Columbian Cultures on the Current Territory of Peru
### The Sun Worship in the Pre-Iaca Cultures
#### 2.3.4 Chincha  
However, there are not only buildings or structures that have an astronomical orientation. Recent archaeological research on the south coast of Peru in the central Chincha Valley examined a geoglyph complex from the Late Paracas culture (approximately 400-100 BC). The complex consists of straight lines, rock features, ceremonial mounds and settlements spread over an area of about 40 km\({}^{2}\). In Chincha, it has been proven that many lines, platform mounds and walls point to the June solstice sunset at an orientation of 294 degrees. These results were confirmed through direct field observations during the June solstice [15].  
Since the Nasca culture emerged from the Paracas culture, it is reasonable to assume that some of the Nasca Lines are also oriented to the Sun.
## 3 Results
### Quantification of the Computational Efficiency Gain
This means that the computation of S-AFM images during the rigid-body fitting of the F1 structure into an actual AFM image (with our chosen parameters and hardware setup) would on average take 212 min, versus only 25 min required with the XYZ-F2 implementation. The results of the computation times obtained for the relaxin protein, i.e., (1.88 \(\pm\) 1.03 ms) for the XY-F method, (1.46 \(\pm\) 0.66 ms) for the XYZ-F method, and (2.47 \(\pm\) 2.04 ms) for the XYZ-F2 method, clearly demonstrate the irrelevance of atom filtering for the simulated AFM of very small molecular structures. The numerical investigations lead us to the following conclusions. Generally, our developed atom-filtering methods significantly speed up the calculation of simulated AFM images. The efficiency gain depends on the size and shape of proteins, and is apparently larger for cases in which the number of surface atoms is small, as compared to the total number of atoms. To clearly illustrate this aspect, we represent in Figure 3B the results discussed above, employing the ratio of surface atoms and all protein atoms as a coordinate. We emphasize again that for very large protein structures, the calculation time of a simulated AFM image can be reduced by about two orders of magnitude by the application of the developed filtering method. While, for proteins with typical sizes of \(\sim\)10 nm, a significant speed up in calculation can be realized, the filtering methods become irrelevant for very small proteins with a generally larger surface atom ratio. Figure 3: **Quantification of the computational efficiency gain by atom filtering methods. ****(A)** Molecular structures of the investigated proteins are shown in the Van der Waals representation on the left side. The number of atoms is given, and their sizes are indicated. For each protein, distributions of the computation time required to execute the filtering method and to calculate the simulated AFM image for molecular orientations obtained from exhaustive sampling are shown as histograms for the XY-F (orange color), XYZ-F (blue color), and XYZ-F2 (green color) atom filtering methods on the right side. **(B)** The same results are represented, employing the surface atom ratio as a coordinate. For each filtering method, the data are shown as mean values and standard deviations of its computation time distribution (solid circle and error bar). For the upper row data (virus capsid, F1-ATPase, F-Actin (avg), relaxin), the surface atom ratio was obtained as an average of all considered molecular orientations. The bottom row data are for the actin filament considered in the upright, diagonal, and flat orientations with respect to the AFM substrate surface. For all plots of F-actin, data of the same scale were used for better comparisons.
# A Framework for the Joint Placement of Edge Service Infrastructure and User Plane Functions for 5G
## 4 Framework Proposal
### Edge Node Placement Stage
Given the latency and reliability constraints and the need to satisfy all TG demands in a capacity-dependent cost model, the combinations cannot be split to reduce computationtime. Additionally, ultra-dense networking and 5G strict requirements will presumably push the EN deployment to thousands of nodes in large cities. Taking this into consideration, exact methods were dropped as solutions for high-scale scenarios and two heuristic methods were implemented for performance analysis purposes: an extended and adapted variant of the **Hybrid Simulated Annealing (HSA)** presented in [23] and an **Evolutionary Algorithm**[24]. These heuristics were selected due to their flexibility when being adapted to solve heavily constrained NP-hard problems. Moreover, both evolutionary techniques and Simulated Annealing have been widely employed to solve FLPs and server placement problems [25; 26; 27]. The HSA in [23] was modified in order to answer the requirements of the formulation summarized in (1) and the related constraints. The results of our simulated testbed to analyze the performance of both methods can be found in Section 5.1.
## 4 Experimental Section
### Encoding Sequence and LED-OFC Recognition Result Analysis  
To explore the effect of encoding sequence on LED-OFC recognition, a six-digit encoding sequence was used. In addition, since the encoding sequence was transmitted cyclically, there were some sequences that are duplicated. Based on this, the non-repeating sequences were selected, which is shown in Table 2. The other parameters were as follows: the frequency was 1000 Hz, the phase difference was 0\({}^{\circ}\), and the distance was 20 cm. For each encoding sequence, about 300 images were acquired for training and 30 images for testing. The final experiment result is shown in Figure 13.  
Figure 12: The recognition accuracy rate at different phase difference resolution.  
As seen above, the recognition accuracy was high enough; most sequences achieved a 100% accuracy rate, and the recognition accuracy of all encoding sequences was above 95%. As for the relatively low accuracy (96.43% and 95.83%), recognition errors may have been caused by jitter during shooting or other reasons, but, in general, the recognition accuracy was completely acceptable. Moreover, the length of the encoding sequence can be changed, and encoding sequences of different lengths will not produce the same LED-OFC; this indicates that, in theory, many different encoding sequences (including encoding sequence length and encoding sequence bits) can be used to generate many different LED-OFCs which could be recognized with high accuracy, and, in the above experiment, only a six-digit encoding sequence was used to prove the feasibility.
## 3 Optical Model Comparison and Evaluation of Influences on the Incident Irradiance on the PV Module
### Ground and Nearby Surfaces' Reflectivity Influence
It was found that an increase in height led to an increase in \(G_{\text{rear}}\) uniformity (i.e., a decrease in \(NU\)). For reference, a one-module string with a height of 1 m guarantees \(NU<\) 5%. Next, three scenarios were considered. Each one was characterized by the number of rows (equal to the number of scenarios), and \(NU\) was observed at varying numbers of modules per string. For all the scenarios, \(GCR\) was set to a constant equal to 0.5, the module under consideration was the center-row module, and for the two-row scenario, the observed row was the front one. \begin{table}
\begin{tabular}{c c} \hline \hline
**Material** & **Albedo Coeff. (-)** \\ \hline Grass & 0.15–0.25 \\ Fresh grass & 0.26 \\ Wet snow & 0.55–0.75 \\ Dry asphalt & 0.09–0.15 \\ Wet asphalt & 0.18 \\ Concrete & 0.25–0.35 \\ Aluminum & 0.85 \\ White ethylene tepolymer & 0.80 \\ Light soil & 0.30 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Materials and respective albedo coefficients. Retrieved from [28; 43; 72]. The results, reported in Figure 10, show that \(G_{\text{rear}}\) decreased when the number of rows increased, by 12.3% and 30.2% when the number of rows was increased from one to two and from two to three, respectively. Moreover, \(N\)\(U\) increased by 12.3% and decreased by 32.8% when scenario 2 was compared with 1 and scenario 3 with 2, respectively. Therefore, a higher number of strings means more ground shaded, thus a lower value of \(G_{\text{rear}}\), but the same behavior does not characterize \(N\)\(U\). In support of the simulation data, [PERSON] et al. in [78] measured the edge effect caused by deploying two adjacent strings along a sunny day in Albuquerque, NM, USA. The two strings were arranged in parallel to mimic the south-north increase in the number of modules per string. It was observed that the easternmost string suffered from the shade of the second string at sunrise, while the reverse was true at sunset. Modeling the ratio \(G_{\text{rear}}/G_{\text{front}}\) for the two strings showed that shading by the other string could affect the ratio by up to 2%.
## 3 Results and Discussion
### Optimal Allocation Considering Return Flow (Model II)
#### 3.3.1 Optimization Results of Irrigation Benefits (Model II)  
Table 11 presents the results of net profit optimization across different time periods. After irrigation with return flow resources, the net profits for the current years, the short-term period, and the long-term period are USD [66.76, 86.95], [66.9, 87.09], and [67.03, 87.23] million, respectively. These figures represent increases of USD [7.14, 7.97], [5.08, 5.63], and [4.4, 4.81] million when compared to net profits without return flow. The growth of net profit diminishes from the current years to the future years, as the potential for increasing crop planting area is higher in the current years (2.6 thousand ha) compared to the future years (1.9 thousand ha in the short term and 1.6 thousand ha in the long term). Once the return flow resources are fully utilized, the irrigation area reaches its maximum capacity, leading to a decrease in net profit growth in the planning years.  
Table 12 presents the results of crop net profit optimization across different periods. It is observed that only the net profits of corn and flax increase after irrigation using return flow resources, while the net profits of other crops remain unchanged. This is because the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Current** & **Short-Term** & **Long-Term** \\ \hline Ignore return flow & [59.62, 78.98] & [61.81, 81.46] & [62.64, 82.42] \\ Consider return flow & [66.76, 86.95] & [66.9, 87.09] & [67.03, 87.23] \\ Net profit growth & [7.14, 7.97] & [5.08, 5.63] & [4.4, 4.81] \\ \hline \hline \end{tabular}
\end{table}
Table 11: Optimization results of total net profit (million USD) for different periods.  
Figure 10: Water allocation optimization results for various crops during different periods.  
water productivity of corn and flax is high, and the return flow resources are preferentially allocated to crops with higher water productivity to enhance irrigation efficiency.
###### Abstract  
To investigate the dynamic wave propagation characteristics and dynamic response of heterogeneous layered slopes under a blasting vibration, a modeling method considering the slope's layered dip angle and heterogeneity was proposed. Different dip jointed slope models were established using the Weibull random distribution function introduced to realize the stochastic distribution of rock mechanics parameters, representing heterogeneity. Taking the background project of the Sijiaying Yanshan Open-Pit Iron Mine as an example, through numerical simulation, the effects of different joint dip angles and rock hardness on the slope's dynamic response were analyzed in detail. The sensitivity of the elastic modulus, cohesion, and friction angle to the slope dynamic response was also investigated. A comparative analysis of the amplification effects between a jointed slope and heterogeneous slope was conducted. Finally, the dynamic stability of the jointed slope and heterogeneous slope under a blasting load was analyzed. The results indicate that the Peak Ground Acceleration (PGA) of jointed slopes with dip angles of 45\({}^{\circ}\) and 60\({}^{\circ}\) is generally higher than that of slopes with a 0\({}^{\circ}\) dip angle and without joints. The smaller the rock mass heterogeneity, the smaller the PGA at the measuring points, and the less sensitive the PGA is to variations in the three quantities. Under the same physical and mechanical parameters of the rock, the amplification factor of jointed slopes is generally greater than that of heterogeneous slopes. Under the blasting load, the overall dynamic time-series safety factors of both slopes decrease first and then increase, with the safety factor reaching its lowest value at the location of the strongest blasting vibration wave. This study can provide guidance for the blasting design and safety protection of layered dip slopes and serve as a reference for the analysis of blasting impact laws in similar mines.  
non-homogeneous slope; dynamic response; slope stability; ubiquitous-joint model +
Footnote †: journal: Applied Sciences  
Article
# Gaussian-UDSR: Real-Time Unbounded Dynamic Scene Reconstruction with 3D Gaussian Splitting
###### Abstract  
Unbounded dynamic scene reconstruction is crucial for applications such as autonomous driving, robotics, and virtual reality. However, existing methods struggle to reconstruct dynamic scenes in unbounded outdoor environments due to challenges such as lighting variation, object motion, and sensor limitations, leading to inaccurate geometry and low rendering fidelity. In this paper, we proposed Gaussian-UDSR, a novel 3D Gaussian-based representation that efficiently reconstructs and renders high-quality, unbounded dynamic scenes in real time. Our approach fused LiDAR point clouds and Structure-from-Motion (SfM) point clouds obtained from an RGB camera, significantly improving depth estimation and geometric accuracy. To address dynamic appearance variations, we introduced a Gaussian color feature prediction network, which adaptively captures global and local feature information, enabling robust rendering under changing lighting conditions. Additionally, a pose-tracking mechanism ensured precise motion estimation for dynamic objects, enhancing realism and consistency. We evaluated Gaussian-UDSR on the Waymo and KITTI datasets, demonstrating state-of-the-art rendering quality with an 8.8% improvement in PSNR, a 75% reduction in LPIPS, and a fourfold speed improvement over existing methods. Our approach enables efficient, high-fidelity 3D reconstruction and fast real-time rendering of large-scale dynamic environments, while significantly reducing model storage overhead.  
autonomous driving; 3D reconstruction; dynamic scene understanding; neural scene representation; 3D Gaussian splitting +
Footnote †: journal: Applied sciences  
0000-0001-9307-3300][PERSON]  
1  
202515626
## 4 Experimental Set-Up
### Evaluated Deep Learning Models for EEG-Based Classification  
For evaluation, we consider three well-established DL models, each recognized for their specialized architectures tailored to EEG signal analysis. These models are designed to ensure efficient feature extraction, robustness, and adaptability in MI tasks [36]. Figure 4 provides a concise visual overview of the evaluated MI-EEG architectures:  
* _ShallowConvNet_[77]: A low-complexity architecture that emphasizes early-stage feature extraction through sequential convolutional layers, square and logarithmic non-linearities, and pooling operations. It effectively emulates the principles of the classical _FBCSP_ pipeline within an end-to-end trainable deep learning framework, offering robust performance in MI classification tasks.
* _EEGNet_[38]: A compact, parameter-efficient model that utilizes depthwise and separable convolutions to disentangle spatial and temporal features. Designed for cross-subject generalization and computational efficiency, _EEGNet_ maintains competitive accuracy across a wide range of EEG-based paradigms.
* _TCNet Fusion_[39]: A high-capacity architecture that incorporates residual connections, dilated convolutions, and 1\(\times\)1 convolutions to construct a multi-pathway fusion network. Its hierarchical design captures long-range temporal dependencies and enhances feature integration across time, improving classification performance in complex MI scenarios.  
As noted previously, an additional methodological consideration involves ranking subjects in descending order based on their individual classification accuracy. Since each deep learning model yields a different subject-wise ranking, the ordering derived from the classical feature extraction method _FBCSP_ is adopted as a consistent baseline reference for comparative performance evaluation.  
Figure 3: Montage Reduction: spatial arrangement of the four EEG electrode montages used in this study, based on the international 10–10 system: 8, 16, 32, and 64 channels.
## 2 Data and Methods
### Solar-Geomagnetic Impact Exclusion  
Solar and geomagnetic activities significantly impact the ionosphere, especially in equatorial and polar regions, making it challenging to differentiate between pre-seismic and solar-geomagnetic ionospheric phenomena [54]. To exclude potential influences associated with solar and geomagnetic activities, many indicators including Dst, Kp, and F10.7 were used in this study. Dst monitors equatorial ring current fluctuations (\(-\)200 to 200 nT), with negative values suggesting magnetic storms and positive values showing magnetic field recovery. The Kp\(\times\)10 index (0-9) indicates overall magnetic conditions as measured at 13 Northern and 12 Southern Hemisphere stations. The F10.7 index monitors solar radio emissions (70 to 250 sfu), which represent solar activity levels. All daily averaged indices were obtained from OMNI Web ([[https://omniweb.gsfc.nasa.gov/form/dx1.html](https://omniweb.gsfc.nasa.gov/form/dx1.html)]([https://omniweb.gsfc.nasa.gov/form/dx1.html](https://omniweb.gsfc.nasa.gov/form/dx1.html)), Accessed on 18 January 2024). Geomagnetic weather (8-48 h) influences the global ionosphere, whereas seismic disturbances impact the ionosphere and are detected within 2000 km of the possible epicenter. Seismic-ionospheric disturbances occur three to four hoursbefore an earthquake [15; 55]. Table S1 and Figure 3 provide information on the exclusion of space weather disturbances. Disturbed days were identified based on criteria such as Dst > 30 nT, Kp\(\times\)*10 > 40, and F10.7 > 130 Sfu. Figure 3a shows variations in these indices from 15 September to 5 November 2015, while Figure 3b displays variations from 15 August to 25 October 2023. Days affected by space weather disturbances were excluded from seismic disturbances by considering the presence of disturbances in GPS, GIM TEC, Te, or Ne data. If any of these parameters exhibited anomalies on a particular day, they were not considered a seismic disturbance. This approach ensured that only seismic-related anomalies were retained for analysis, enhancing the accuracy of the results.
## 3 Results
### 2 nd Data (FFT) Analysis  
To resolve this issue, the SVMs were converted into frequency bands via FFT to classify the data obtained when the safety helmet was worn properly, not worn, and worn improperly. An FFT involves the same formula as a discrete Fourier transformation (DFT) as expressed in Equation (2), but the data size is made 2\({}^{\text{n}}\) to improve the calculation speed [20]. Here in, \(\text{x}_{[\text{n}]}\) is the data of SVM values, \(\text{e}^{-\frac{2\pi\text{i}}{\text{N}}\text{kn}}\) is a twiddle factor as a complex exponential function, and j is an imaginary unit. When an FFT is used in analysis, the sizes of the frequency bands for each activity are distributed differently. Therefore, the proper wearing, improper wearing, and not wearing data for each activity can be identified [21; 22].  
\[\text{X}_{[\text{k}]}=\sum_{\text{n}=0}^{\text{N}-1}\text{x}_{[\text{n}]} \text{e}^{-\frac{2\pi\text{i}}{\text{N}}\text{kn}}=\ \sum_{\text{n}=0}^{\text{N}-1}\text{x}_{[\text{n}]} \left(\cos\left(\frac{2\pi\text{kn}}{\text{N}}\ \right)+\text{jsin}\left(\frac{2\pi\text{kn}}{\text{N}}\ \right)\right)\ (\text{k}=0, \ldots,\text{N}-1) \tag{2}\]  
[Figure 10] shows the results of using Equation (2) on the data in Figure 9 and conversion via FFT. To perform an FFT, 2\({}^{\text{n}}\) data are needed, so the walking activity data were separated into On and Off states, and the number of data was set to be the same at 64. In Figure 10, there is a pattern in which both the On and Off states of the walking activity are prominent in the 0.85-1 Hz range. Near 1 Hz, the slope of the graph changes gradually for the On data but changes rapidly for the Off data.  
\begin{table}
\begin{tabular}{c c c} \hline  & **Walking (On)** & **Walking (Off)** \\ \hline Average variation (Magnitude) & 257.86 & 258.89 \\ Dispersion (Magnitude) & 1865.85 & 2511.79 \\ Observed number & 100 & 100 \\ Degrees of freedom & 99 & 99 \\ F proportion & 0.7428381 & \\ P (F \(\leq\) f) one-sided test & 0.0704417 & \\ F-rejection value one-sided test & 0.7721233 & \\ \hline Result & F-rejection value \textgreater F proportion \\ \hline \end{tabular}
\end{table}
Table 3: F-test results for SVM during walking according to whether or not the chinstrap on the safety helmet was fastened.
# Multi-Decision Dynamic Intelligent Routing Protocol for Delay-Tolerant Networks
## 1 Introduction
Combining the historical interaction information between nodes and considering the global network, nodes are classified into three types of relationships: friends, colleagues, and strangers. (3) Reinforcement learning is combined with node relationships as well as message attributes to decide the best next hop during the routing process to achieve dynamic intelligent routing decisions. The rest of this paper is organized as follows: In Sections 2 and 3, routing decisions in a double Q-learning environment and dynamic decisions based on node relationships in DTNs are described, and a multi-decision dynamic intelligent routing protocol is proposed. The performance of the proposed protocol is evaluated and compared with conventional protocols in Section 4. Section 5 discusses the impact of the proposed protocol and some potential limitations. Finally, Section 6 concludes this work and proposes future research directions. To clearly describe the proposed routing protocols in the following sections, we provide a list of notations and abbreviations in Table 2. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Name of Protocol** & **Routing-Based** & **Shortcomings** \\ \hline Prophet [3] & history & High drop ratio \\ PPUR [4] & history & Low delivery rate when network resources are limited \\ FFHBR [5] & history & High overhead \\ EMRT [6] & history & Low delivery rate when network resources are limited \\ UADT [7] & community & Buffer congestion \\ SC-AMSW [8] & community & High overhead \\ HSBR [9] & community & High overhead when network resources are limited \\ CTML [10] & community & Performs poorly when the network topology \\ Proposed in [14] & Q-learning & Low delivery rate \\ ARSIDQL [15] & Double Q-learning & Low delivery rate \\ DQLR [16] & Double Q-learning & Low delivery rate when network resources are limited \\ ORAL [17] & Double Q-learning & Low delivery rate when network resources are limited \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of related routing protocols.