esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Column (Breakthrough) Study  
The study aimed to verify the recyclability of rGO-Fe\({}_{2}\)O\({}_{3}\) and establish the boundaries necessary to design manufacturing-scale adsorption columns. Several adsorption-regeneration breakthrough cycles were performed at \(22\pm 1\)\({}^{\circ}\)C using a column operating in the adsorption-desorption rhythm. Upon completing the breakthrough experiment, the adsorbed oil was recovered from rGO-Fe\({}_{2}\)O\({}_{3}\) using ethanol. All experiments were conducted at \(22\pm 1\)\({}^{\circ}\)C and a 3.3 mL/min emulsion flow rate. The mass of rGO-Fe\({}_{2}\)O\({}_{3}\) varied between 0.04 and 1 g, and the initial oil concentration was 100 ppm. When the rGO-Fe\({}_{2}\)O\({}_{3}\) adsorption saturated, 30 mL of ethanol was added into the column at 3.3 mL/min, followed  
Figure 4: (**a**) Freundlich adsorption isotherm fitting of the equilibrium data (time = 30 min, T = 22 \({}^{\circ}\)C) and (**b**) pseudo-second-order kinetic model fitting of the kinetic data (C\({}_{0}\) = 100 ppm, dose = 5 mg, and T = 22 \({}^{\circ}\)C).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **rGO-Fe\({}_{2}\)O\({}_{3}\)** \\ \hline \(n\) & 1.6 \\ \(k_{t}\) & 23 \\ R\({}^{2}\) & 0.922 \\ \(q_{t}\) & 1301 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Freundlich adsorption isotherm parameters of rGO-Fe\({}_{2}\)O\({}_{3}\) for the removal of emulsified oil.  
by washing using water to eliminate residual ethanol. The emulsion was collected from the exit of the fixed-bed outlet every 10 min, the oil concentration was measured, and the breakthrough curves were generated (Figure 5). The upper area above the breakthrough curve reflects the fixed-bed capacity and is given by [53]:  
\[q_{e}=\frac{GxC_{0}}{m}\int_{0}^{t_{\mathrm{s}}}(1-C_{t}/C_{0})dt \tag{8}\]  
where \(G\) is the flow rate (L/min), \(C_{0}\) and \(C\) are the inlet and outlet concentration (mg/L), and \(t_{\mathrm{s}}\) is the time to complete saturation of the bed.  
It is noted that removal efficiency (90%) did not reach 100 at the beginning of each cycle due to the short residence time (<1 min), which was due to the high flow rate. The effect of regeneration on the dynamic adsorption capacity of rGO-Fe\({}_{2}\)O\({}_{3}\) is shown in Table 4. The capacity declined by 5.7% after the second cycle and 17% after the third cycle. Moreover, as shown in Table 4, the adsorption capacity of rGO-Fe\({}_{2}\)O\({}_{3}\)-NC was higher than that of GO. Regardless of the cycle number, the final capacity retention after three cycles of rGO-Fe\({}_{2}\)O\({}_{3}\)-NC was higher than GO [9]. Although there was a 17% reduction in adsorption capacity, the performance could be optimized by extruding the nanocomposite into porous pellets, a process routinely carried out for adsorbents and catalysts to preserve the mechanical integrity of the adsorbent.
# An Enhanced Tree Routing Based on Reinforcement Learning in Wireless Sensor Networks +
## 3 System Model and Problem Statement
### Cognitive Metrics
#### 3.2.3 Weighted Average of Buffer Occupancy Ratio  
If multiple nodes select the same neighbor as a parent node, the selected parent node will be overloaded. In other words, a buffer overflow may occur when the parent node receives more data than it can handle. To prevent congestion, we adopt the buffer occupancy  
Figure 1: An example: deployment of sink nodes and sensor nodes.  
ratio as a decision variable to select a parent node. To prevent sudden changes in measured data, we define a \(n\)-period WMA of the buffer occupancy ratio (\(B\)) for neighboring node \(i\) as,  
\[B_{i}=\frac{(w_{1}*B_{i}^{t-1}+w_{2}*B_{i}^{t-2},\ldots,+w_{n}*B_{i}^{t-n})}{(w_ {1}+w_{2},\ldots,+\ldots w_{n})}, \tag{2}\]  
where \(B^{t}\) is the buffer occupancy ratio at time \(t\), and is defined as  
\[B^{t}=\frac{B_{cur}}{B_{max}}, \tag{3}\]  
where \(B_{cur}\) is the current buffer size at time \(t\), and \(B_{max}\) is the maximum buffer size.
# Reliability Analysis of Response-Controlled Buildings Using Fragility Curves
## 3 Fragility Curve Based on Incremental Dynamic Analysis
### Response Energy of the Dampers  
Hysteretic dampers dissipate energy through the inelastic deformation of metallic substances. On the other hand, oil dampers use the orifice flow resistance mechanism as a response to energy. For nonlinear systems, the equation of motion can be expressed as [22]:  
\[[M]\big{\{}\ddot{u}\big{\}}+[c]\big{\{}\dot{u}\big{\}}+Q(u,\dot{u})=-[M][u]\begin{cases} \dot{\tilde{x}}_{0}\\ \dot{\tilde{Y}}_{0}\\ \dot{\tilde{Z}}_{0}\end{cases}=\{p\} \tag{2}\]  
where \([u]\) is the displacement vector, \([M]\) and \([C]\) are the mass and damping matrices, \(\{u\}\) and \(\{p\}\) are the displacement and external force vectors, respectively, and \(Q(u,\dot{u})\) is the nonlinear restoring force vector.  
The equation of energy can be derived by multiplying the velocity vector, \(\big{\{}\dot{u}\big{\}}^{\mathsf{T}}\), and integrating by the time range [0-t]:  
\[W_{K}+W_{D}\ +W_{P}\ =W_{I}\ \ \text{Equation of energy} \tag{3}\]  
\[W_{K}=\frac{\big{\{}\dot{u}\big{\}}^{\mathsf{T}}[M]\big{\{}\dot{u}\big{\}}}{2 }\ \text{Kinematic energy} \tag{4}\]  
\[W_{D}\ =\int_{0}^{t}\big{\{}\dot{u}\big{\}}^{\mathsf{T}}[C]\big{\{}\dot{u} \big{\}}dt\ \ \text{Damping energy} \tag{5}\]  
\[W_{P}\ =\int_{0}^{t}\big{\{}\dot{u}\big{\}}^{\mathsf{T}}\,Q\big{(}u,\dot{u} \big{)}dt\ \ \text{Potential energy} \tag{6}\]  
\[W_{I}\ =\int_{0}^{t}\big{\{}\dot{u}\big{\}}^{\mathsf{T}}\{P\}dt\ \ \text{Input energy} \tag{7}\]  
The STERA 3D software also [20] calculates the energy absorption rate of the structural members and the dampers as shown in Equation (3). The average rate of energy absorption by hysteresis and oil dampers with increasing values of ground motion intensity was calculated by the software as shown in Figure 8. It can be seen that the hysteresis damper's energy absorption rate gradually increases with increasing ground motion intensity and decreases after reaching its maximum absorption capacity. On the other hand, oil dampers store energy that is significant for smaller intensities of ground motion, but their capacity will decreases at higher intensities.  
Figure 8: Energy response of hysteresis and oil dampers for TS models.
# Lunar Satellite Constellations in Frozen Low Orbits
###### Abstract  
This research studies the potential of frozen low lunar orbits to be used in the design of constellations for global and regional communication or navigation. We introduce a robust two-stage approach to the frozen low lunar orbit design based on the successive application of non-gradient techniques, the Bayesian optimization and the Nelder-Mead method. The developed methodology has a number of advantages over existing numerical design techniques and allows revealing orbits with the periodic behavior of the eccentricity vector over long propagation intervals in the full dynamical model. By leveraging a convenient nomogram with constellation visibility parameters and lower bound coverage curves, we have identified most suitable low-altitude orbital configurations of Walker type and then adjust them to be frozen. The frozenness condition can be achieved without changing the orientation of orbital planes. Visibility and coverage metrics (multiplicity of continuous coverage for specified sites, polar regions, or the whole lunar surface; position dilution of precision) of candidate constellations are analyzed. Several promising designs of frozen constellations in near-circular low lunar orbits are singled out. The frozen orbit stability and the station-keeping cost are discussed.  
satellite constellation; Walker-Mozhaev constellation; low orbit; frozen orbit; Bayesian optimization; Nelder-Mead method Article
# Rainfall Induced Landslide Studies in Indian Himalayan Region: A Critical Review
## 3 Results
### Lake Damming Landslides  
Landslide lakes or dams are temporary lakes in the river valleys formed after a landslide blocks the river course. Landslide dammed lakes and their outburst floods (LLOFs) are common in the Indian Himalaya. The breaching of such temporary lakes with a huge amount of accumulated water and sediments can create devastating floods in the downstream areas [61]. In the Himalayas, landslide dams commonly form in high mountains because of different mass movement types such as rock and debris avalanches, rock and soil slides, mud-debris-earth flows. The oldest recorded landslide lake was the Gohna Tal, which was formed in September 1893 as a result of heavy precipitation blocking the Birahi Ganga River situated in the Kumaon Himalaya. Gradually, the lake expanded to an approximate 4000 m length, 340 m width, and 300 m depth. On 26 August 1894, the dam collapsed, and a devastating flood hit downstream of Birahi Ganga [62].  
The techniques used for assessment of damming processes are either based on geographic characteristics, hydraulic properties, and velocity measurement of dam material. The assessments of the damming process are dependent on the analysis of three significant factors: (a) pre-dam formation mechanism; (b) dimensional characteristics of the dam; and (c) stability analysis of dam [63]. The pre-dam formation mechanism largely depends on the slope stability analysis and landslide triggering factors, whereas dimensional characteristics are dependent on geometry of the area and landslide volume. In terms of slope stability, the techniques can be categorized as discontinuum modeling and continuum modeling [63]. Discontinuum modeling is majorly used for rock slope stability analysis, whereas continuum modeling is used for debris flows as well as rock slopes dependent on the material and geometry of the slope [19; 64].  
Most of the studies have focused on geomorphic analysis [15; 16; 57; 63] using different satellite images to understand the spatiotemporal landslide changes. [PERSON] and [PERSON] [65] catalogued the LLOFs developed in the Trans-Himalayan region between 2000 and 2005 and studied its impact on the stability in the region. [PERSON] et al. [57] conducted an extensive investigation of Phutkal River landslide dammed lake for landslide occurrence on 31 December 2014 using multi-temporal Cartosat-2 images of 1 m spatial resolution and calculated slide volume using pre- and post-event datasets. [PERSON] et al. [63] used GE images of 1.5 m resolution and estimated landslide volume along with slope stability analysis for Urni landslide in Himachal Pradesh.
## 4 Discussion
### The Need for Integration of New and High-Resolution RS Datasets and High Quality Reference Data
While sampling plays a key role in mapping agricultural systems, obtaining reliable data, for instance from remote areas, can be difficult [144]. Addressing these gaps could involve sharing databases or using very high-resolution satellite imagery as validation. Mapping croplands requires extensive and up-to-date training and validation datasets, which is why [PERSON] et al. [160] for instance, proposed a mapping approach tailored to overcome the limitations of the agricultural systems of the Sudan-Sahel region.
# A Terminal-Oriented Distributed Traffic Flow Splitting Strategy for Multi-Service of V2X Networks
## 3 V2X Networking Architecture  
The V2X communication scenario of vehicular networks is modeled in Figure 1, which is primarily divided into in-vehicle (IN-V) communication and out-of-vehicle communication (OUT-V). The IN-V communication is mainly based on the V2T (Vehicle to Terminal) installed in the car, which carries out vehicle environment (V2E) monitoring, and performs OBD (on-board diagnostics) diagnosis, in-vehicle video monitoring, positioning, and card swiping management, among other functions. It also carries out access communication and service of the mobile terminal in the vehicle (V2 MT). V2B (Vehicle to Business) refers to the commercial information operation service in the vehicle. Extravehicular communication is also based on the vehicle terminal, which mainly implements environment awareness including V2P (Vehicle to Pedestrian) and V2I (Vehicle to Information) service requirements while driving, and realizes V2V communication with other vehicles to achieve safe driving.  
In a 5G vehicle networking scenario, V2X communication includes V2E and V2 MT communication in the vehicle, as well as V2E, V2V, and vehicle to road side) communication (V2R) outside the vehicle. Considering the demand for off-board communication of the vehicle V2X service, the traffic flow splitting strategy (TFSS) is run in each V2T terminal (software-based embedded application), and the TFSS is managed by the access controller (AC) cloud, as illustrated in Figure 2. In order to better support the vehicle information service, the AC cloud needs to connect to the portal server and the user authentication (UA) server, provide content to the in-vehicle information service, and verify the service user. The business operation support system (BOSS) conducts operational billing and customer management for vehicle services.  
Figure 1: Communication model in V2X Scenario.
## 2 Methodology
### Distance-Weighted Structure-Oriented Paired Model Trace Construction  
To begin with, a certain number of direction pairs are set to form the model trace. The directions perpendicular to each other are considered as a pair, as shown in Figure 2b with straight lines of the same color. Note that it may be necessary to form some pseudo seismic traces as hollow circles, as shown in Figure 2b. It can be calculated via the inverse distance weighted averaging method, leveraging the two adjacent traces of each hollow circle. Then, all seismic traces, including the pseudo ones, are assigned weights based on the distance from the center trace. In this paper, the Gaussian function is used to weight the seismic traces. Greater weight is assigned to the adjacent traces of the center trace, while smaller weight is allocated to those farther away from it.  
To form a model trace in each direction, the inverse distance weighted averaging method is also adopted, while the center trace is removed, as shown in Figure 2c. Subse  
Figure 2: Illustration of the original lateral analysis window (**a**) and the lateral analysis windows overlaid by user-defined directions (**b**) and after interpolation (**c**). Note that the red circle in (**a**,**b**) denotes the center trace, and the empty circles in (**b**) denote traces that need to be interpolated by their most nearby seismic traces. The colors of the circles in (**c**) are associated with the weights indicated by the color bar. Colored lines in (**b**,**c**) represent different directional pairs.  
quently, recalculate the weights to ensure the sum of seismic trace weights in each direction equals one. For the lth direction, the weights are recalculated as  
\[w_{l,j}=\frac{w_{l,j}}{\sum_{j=1}^{l_{l}}w_{l,j}},\,j\
eq\frac{J_{l}+1}{2} \tag{9}\]  
where \(J_{l}\) is the number of seismic traces in the \(l\) direction, and \(w_{l,j}\) is the weight of the \(j\) trace in the \(l\) direction. It must be pointed out that the number of seismic traces in each direction is singular, and the trace number of the center trace in the \(l\) direction is always \(\frac{J_{l}+1}{2}\). However, since the center trace is removed during model trace construction, the number of traces involved in model trace calculation is even. The model trace in \(l\) direction can be written by following  
\[\hat{\textbf{y}}_{l}=\sum_{j=1}^{J_{l}}w_{l,j}\textbf{y}_{l,j},j\
eq\frac{J_{l }+1}{2} \tag{10}\]  
where \(\textbf{y}_{l,j}\) is the \(j\) trace after dip correction in the \(l\) direction. The model trace construction is essentially to predict the center trace in different directions.
## 3 Results
### System Performance Comparison  
Figure 5 presents a comparative analysis of the four scenarios described above, using the three antennas, that tests their performance in terms of the bit error rate versus distance when a wireless communication takes place. These results are also very useful for understanding how capable different antenna configurations/devices are of preserving the signal quality as transmission distances get longer.  
Scenario 1, utilizing commercial dipole antennas for both TxU and RxU, serves as the baseline and shows the poorest performance. The bit error rate rapidly increases to approximately 90% at 60 m. In contrast, Scenario 4, employing the LaYUA for TxU and the TuSA for RxU, demonstrates a remarkable improvement. At 60 m, Scenario 4 maintains a bit error rate of only about 10%, representing a significant improvement over Scenario 1. This better performance extends to the maximum tested distance of 100 m, where the Scenario 4 error rate remains below 40%, while Scenario 1's approaches 100%, indicating an approximately 60% improvement in signal integrity at long range.  
Figure 4: Measured (solid blue and dotted yellow) and simulated (dashed red and dash–dot violet) antenna results. (**a**) Reflection coefficient magnitude. (**b**) Normalized radiation pattern on the azimuth plane (\(\phi=90^{\circ}\)) at 2.4 GHz. The results are invalid in a sector between \(50^{\circ}\) and \(180^{\circ}\) due to the measurement setup.  
Figure 5: BER obtained from measurements. Scenario 1 (solid blue): commercial dipole (TxU)–commercial dipole (RxU). Scenario 2 (dashed red): LaYUA (TxU)–commercial dipole (RxU). Scenario 3 (dotted yellow): commercial dipole (TxU)–TuSA (RxU). Scenario 4 (dash–dot violet): LaYUA (TxU)-TuSA (RxU).  
Scenario 2 and Scenario 3 offer intermediate improvements. At 60 m, Scenario 2 shows a bit error rate of 50%, an approximately 40% improvement over Scenario 1. Scenario 3 performs better that Scenario 2 up to 60 m but degrades rapidly beyond this point. At 80 m, Scenario 3 exhibits a bit error rate of about 90%, while Scenario 2 maintains one around 60%, demonstrating the 30% improvement of Scenario 2 over Scenario 3 at this distance. Notably, Scenario 4 outperforms all others consistently, showing a 40% lower error rate compared to Scenario 2 and a 70% improvement over Scenario 3 at the 80-meter mark. These quantitative comparisons underscore the substantial impact of antenna selection on wireless communication system performance, with the LaYUA-TuSA combination (Scenario 4) offering the most significant improvements in reliability and range.  
This comparative analysis underscores the significant impact that antenna selection has on wireless communication system performance. Scenario 4, employing the LaYUA-TuSA configuration, demonstrates the most robust performance in terms of reliability and range, offering the lowest bit error rate, even at long distances. This makes it the most suitable for applications requiring strong signal integrity over large distances.
# Optimizing the Parameters of Long Short-Term Memory Networks Using the Bees Algorithm
## 1 Introduction
Hence, the paper aims to improve the performance of the LSTM in dealing with sequential problems by optimizing the parameters related to the three gates and cell state. This paper proposes a hybrid nature-inspired algorithm, which takes the advantages of the Bees Algorithm (BA), in order to improve the performance of the three gates and cell state. In particular, the BA was used to optimize the learning rate factor so that each part had its own learning rate that was determined based on the global learning rate. Having a more optimum learning rate means more optimum updates for the network weight [11]. Artificial porosity images were used for testing the algorithms; as the input data are images, a Convolutional Neural Network (CNN) was added in order to extract the features in the images and feed the LSTM to predict the porosity in sequential layers of artificial porosity images that mimicked real CT scan images of products manufactured by the Selective Laser Melting (SLM) process. The hybrid Bees Algorithm Convolutional Neural Network Long Short Term Memory, referred to as the BA-CNN-LSTM, combines the best DL networks for dealing with images and sequential data, as the CNN is the most powerful DL network for analyzing images [4]. In addition, the LSTM network is the best algorithm for dealing with sequential data, as it retains the information for a long period by remembering long-term dependencies, which address the problem of normal RNNs [7]. It can be applied in other contexts, such as in signal processing to classify electrocardiogram (ECG) benchmark image data, as will be shown in Section 4. Also, it can deal with time series numerical data, wherein the CNN is not needed in this case, as there is no feature extraction task for the images. The contribution of the paper is improving the performance of LSTM network in predicting sequential data using BA. As the input data are images, CNN is added to extract the image features yielding a hybrid algorithm (BA-CNN-LSTM) that provides a more accurate prediction of porosity percentage appearing in sequential layers of artificial porosity images that mimic CT scan images of parts manufactured by SLM process. The paper structure starts with Section 2, which presents a review of the LSTM while showing the gaps that need to be fulfilled to improve its performance. Then, Section 3 describes the process for the proposed BA-CNN-LSTM, and Section 4 shows the results and discussion of the proposed model. Section 5 presents the study conclusion and future recommendations.
## 2 Proposed Architecture
### Recovery Analysis  
SEUs occur in sensitive nodes, particularly those located near the reverse-biased drain junction of an off transistor, making these nodes particularly vulnerable to transient pulses. Specifically, when an SEU happens at the drain of a PMOS transistor, it produces a positive transient pulse, indicating a transition from '0' to '1', while an SEU at the drain of an NMOS transistor results in a negative transient pulse, signifying a transition from '1' to '0'. In the configuration depicted in Figure 1, the internal node S1, responsible for storing '1', is exclusively surrounded by PMOS transistors. Consequently, the only SEU that can affect S1 is a transition from '0' to '1'. Thus, given the assumed condition where Q = 1, S1 = 1, QB = 0, and S0 = 0, S1 does not qualify as a sensitive node. Instead, this section focuses on SEUs and SEMNUs affecting the sensitive nodes Q, QB, and S0.
## 5 Results and Discussion
### Ablation Study  
Considering the lack of literature methods, to perform a benchmark, an ablation study was performed to confirm the capabilities of the proposed method. Experiments were conducted without IHC individual tile scoring CNN initialization, and using alternative statistical methods for individual tile score aggregation instead of MLP (median and mean), as can be seen in Tables 3 and 4. The results show that these alternatives are, in most settings, less adequate for the task at hand.  
It is noteworthy that the median and mean-based aggregation are followed by a conversion to binary classes (0\(+\) and 1\(+\) are considered negative, while 2\(+\) and 3\(+\) are considered positive), since tiles have four possible labels. According to the guidelines, 2\(+\) cases can be either negative or positive, but in an uncertain diagnosis scenario, it is preferable to classify them as positive.