esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Using ML for Water Activities
### Water Resource Management and Quality Prediction  
#### 3.1.1 Water Resource Management Techniques  
* **Irrigation optimization**[242]: XGBoost is harnessed to optimize irrigation scheduling, particularly in regions like Morocco, aiding in efficient water usage for crop cultivation.
* **Urban groundwater quality**[243]: Leveraging least squares support vector machines (LS-SVM), this study focused on enhancing the quality of urban groundwater. It effectively monitored and predicted groundwater quality, particularly in areas vulnerable to contamination due to urbanization.
* **Water level forecasting**[244]: Multiple ML models, including multilayer perceptrons (MLP), long short-term memory (LSTM), and XGBoost, were employed for accurate water level forecasting. These models contributed significantly to flood warning systems and freshwater resource management.  
Superiority of MLP[245]: Among the models used for water level prediction, MLP emerged as the standout performer. It exhibited a high degree of accuracy, especially in capturing short-term dependencies.
## 3 Proposed Protocol
If preP(A,D) does not exist, the message is forwarded to node B since P(A,D) is not larger than P(B,D). If preP(A,D) exists, the value of P(B,D) is compared with that of preP(A,D). If P(B,D) is larger than preP(A,D), the message is forwarded to node B, and preP(B,D) is set as P(B,D). Otherwise, the message is not forwarded. If the considered message is not the last message, the same procedure applies to the remaining messages. Figure 6 shows an example of the message delivery of the proposed protocol. Node A and node B have the table for the message list as well as the delivery predictability and preP of the other nodes. For example, in Figure 6, node A has messages M1, M2, M3, and M4. Delivery predictability for nodes B, C, D, E, and F are stored but preP is stored only for nodes C, D, and E, since we assume that node A has not exchanged message with node B and F yet. Additionally, node B has messages M5, M6, and M7. Figure 5: A flowchart of the proposed protocol. Delivery predictability for nodes A, C, D, E, and F are stored but the preP information is stored only for C, D, E, and F. As shown in Figure 6b, after contact, message M1 is not forwarded to node B since the delivery predictability of node B is not higher than node A. Message M2 is forwarded to node B since the current delivery predictability and preP for node D, which is the destination node of message M2, are higher than those of node A. M3 is delivered to node B since the destination node of M3 is node B and the information of preP and delivery predictability for node B of node A are updated. Message M4 is forwarded to node B with a rationale similar to the message M2. Only message M7 of node B is forwarded to node A since node A has a higher delivery predictability and preP. Figure 6: An example of the message delivery: (**a**) before contact, (**b**) after contact.
# Design and Analysis of a Low Latency Deterministic Network MAC for Wireless Sensor Networks
## 6 Performance Analysis
### Delay Analysis  
The network delay is defined as total time taken for the generated information to travel when a communication between two nodes in a network takes place. In this paper, we consider the waiting time (\(W_{time}\)), propagation delay (\(P_{delay}\)) and transmission delay (\(T_{delay}\)).  
* \(W_{time}\) is the waiting time for a device until the next shared slot if the packet is generated outside the allotted shared slot period.
* \(P_{delay}\) in a network is the amount of time needed for one bit of data to travel to a one hop destination.
* \(T_{delay}\) is defined as the amount of time required to transmit the total payload with respect to the link data rate.  
Therefore, the average transmission delay in the IEEE 802.15.4e network, when transmission is successful in the \(j\)-th superframe, can be calculated as:  
\[\begin{split} Delay&=\sum_{j=1}^{\infty}(1-P_{success })^{j-1}P_{success}((j-1)BI\\ &\quad+W_{time})\ +T_{delay}+P_{delay}\end{split} \tag{24}\]  
where \(P_{success}\) is the successful data transmission probability and \(BI\) is the beacon interval.
## 4 Results and Discussion
Depending upon the geo location of the study, the interference due to clouds, wind, moisture, temperature, and dust would highly influence the actual light that would reach the sensor. Hence, making a comparison based on the lumens of a celestial object became highly impractical. We could, however, measure the lux of the same during the time of recording, but a Bortle class 5 sky made it next to impossible to obtain accurate results. Instead, we simply studied the exposure required to capture these images to make them comparable. The idea was that if we use a lens of the same aperture as a study in reference, we will just change the number of frames to change the total exposure time and stack the frames later in post-processing. In the experiment performed by [PERSON], one exposure lasted for 30 min. We took 500 frames of 6 s each, equating to a total exposure time of 50 min, resulting in the same level of brightness of the image. This led us to conclude that the two applications are indeed similar in nature. The contributions of our proposed work are presented in the table. The table presents the differences between the existing work and our proposed work (Table 4).
## 3 Results and Discussion
### Connected vs. Total Porosity
\begin{table}
\begin{tabular}{c c c c} \hline
**Reference** & \(\phi_{c}\) **\%** & **Porous Material** & **Relationship Type for** \\  & & & **Determination of \(\phi_{c}\)** \\ \hline
[4] & 3, 9 & Sphere packing, sintered porous media & \(k\sim(\phi-\phi_{c})^{q}\) \\ \hline
[6] & 4 & Hot-pressed calcite & \(k\sim(\phi-\phi_{c})^{q}\) \\ \hline
[8] & 2.5, 3.5, 4.5 &
\begin{tabular}{c} Fontainebleau sandstone, fused glass \\ beads, hot-pressed calcite \\ \end{tabular} & \(k\sim(\phi-\phi_{c})^{q}\) \\ \hline
[16] & 33 & Randomly placed squares & \(\phi_{con}=a_{1}x^{3}-a_{2}x^{2}+a_{3}x\) \\ \hline
[37] & 9–14 & Basalt clasts & \(\phi_{con}\sim(\phi-\phi_{c})^{p}\) \\ \hline
[17] & 5.9 & Carbonate rocks & \(k\sim(\phi-\phi_{c})^{q}\) \\ \hline
[19] & 0.85 & Microgranite & \(k\sim(\phi-\phi_{c})^{q}\) \\ \hline
[20] & 1.9 & Fontainebleau sandstone & \(k\sim(\phi-\phi_{c})^{q}\) \\ \hline
[21] & 0.855 & Granite & \(k\sim(\phi-\phi_{c})^{q}\) \\ \hline
[18] & 2.4 & Young sea ice & \(\phi_{con}\sim(\phi-\phi_{c})^{p}\) \\ \hline This study (before) & & & \\ Heterogeneous & 7.3 & Carbonate rocks & \(\phi_{con}=a\phi^{2}+b\phi+c\) \\ Naturally fractured & 5.4 & & \\ \hline This study (after) & & & \\ Heterogeneous & 4.8 & Carbonate rocks & \(\phi_{con}=a\phi^{2}+b\phi+c\) \\ Naturally fractured & 6.2 & & \\ \hline \end{tabular}
\end{table} 
Table 4: Percolation threshold porosity of different porous materials.
## 3 Proposed GIS-Based Approach
### PSs Optimal Siting
#### 3.2.1 Multi-Site PSO Algorithm for PSs Location  
The PSO described in Section 2 considers a single site. However, in the case of PSs, we have to deal with multiple sites corresponding to multiple new PSs. Figure 14 shows an example of installing two new PSs, New PS1 and New PS2, in addition to thirteen existing PSs: Cur PS1-Cur PS13.  
Figure 13: Squares available for PSs settlement: green are the squares able to accommodate a PS; red are the squares unable to accommodate a PS; blue is the squares that contain an MV substation without enough space for conversion into a PS, while the orange squares contain either an MV substation with enough space to be converted into a PS or the location of expected coming PSs, as indicated in the Unareti developing plan.  
As depicted in Table 1, the PSO only modifies the location of the two new PSs, i.e., \(\left(x_{i}^{New\ PS1}(t);\ y_{i}^{New\ PS1}(t)\right)\) and \(\left(x_{i}^{New\ PS2}(t);\ y_{i}^{New\ PS2}(t)\right)\), while the location of the current PSs are taken as they are, e.g., \(\left(x^{Cur\ PS1}(t);\ y^{Cur\ PS1}(t)\right)\).  
Moreover, as shown in Table 2, each particle \(i\) has its velocity, and particle's personal best, while a global best characterizes each PSs site. Referring to Figure 14, Table 2 shows the parameter used by the PSO. For simplicity, only generic values of the particle's coordinates are reported, while the other cells have been intentionally left empty.
## 2 The Symmetric Zero-Area Conversion Method  
### The Introduction of SZAC  
Window function \(C\) needs to satisfy symmetry, and its total area is 0, as shown in Expression (1).  
\[\sum\limits_{j=-m}^{m}C_{j}=0,C_{j}=C_{-j} \tag{1}\]  
where \(m\) is half the window width. In this paper, the peak-like function constructed by the Gaussian function is selected as the window function, and its basic form is shown in Expression (2). \(H_{\mathrm{G}}\) is the full width at half maximum (FWHM).  
\[C_{j}=G_{j}-\frac{\sum\limits_{j=-m}^{m}G_{j}}{2m+1} \tag{2}\]  
\[G_{j}=e^{-4\ln{2(j/H_{\mathrm{G}})}^{2}} \tag{3}\]  
\[y_{i}^{*}=\sum\limits_{j=-m}^{m}C_{i}y_{i+j} \tag{4}\]  
Expression (4) describes the conversion process, \(y_{i}\) is the original spectral data, and \(y_{i}^{*}\) is the conversion result. Threshold processing is required before peak identification. According to the \"3\(\delta\)\" rule, define threshold function as Expression (5).  
\[T(i)=\frac{y_{i}^{*}}{\delta y^{*}} \tag{5}\]  
\[\delta y^{*}=\sqrt{\frac{\sum\limits_{i=1}^{n}\left(y_{i}^{*}-y^{*}\right)^{2} }{n}} \tag{6}\]  
\[\frac{-}{y^{*}}=\frac{\sum\limits_{i=1}^{n}y_{i}^{*}}{n} \tag{7}\]  
where \(n\) is the total address number, and \(\delta y^{*}\) is the standard deviation of \(y^{*}\). If the address \(i\) meets \(T(i)>T_{0}\), it is determined to be the peak, and \(T_{0}\) is generally set as 1\(\sim\)2.5.  
The core idea of SZAC is to process spectral data with sliding window function conversion. According to the characteristics of the window function, the conversion result at the peak value is a large positive number, while the non-peak one is small or even negative. On this basis, the peak position can be identified by filtering the threshold value. The threshold function is linear. Its purpose is to reduce the influence of spectral intensity on the conversion results, and unify the threshold-processing results of different orders of magnitude to the order of \(T_{0}\).
## 3 Experimental Results
### Resistance vs. CO Concentration  
From Figure 7a, the electrical resistance has been calculated for our chemical sensor through Equation R = \(\rho\) t/A, and then, the behavior of resistance vs. CO concentration can be estimated and graphed as Figure 8 illustrates.  
Figure 7: Electrical response (resistivity) of NiSb\({}_{2}\)O\({}_{6}\) pellets as a function of (**a**) CO concentration, (**b**) CO operating temperature.  
Observing Figure 8, when the chemical sensor and CO gas make a reaction, the resistance decays for three temperatures, 100, 200 and 300 \({}^{\circ}\)C. The sensor has the highest resistance if both temperatures are 100 and 200 \({}^{\circ}\)C. However, when the temperature is 300 \({}^{\circ}\)C, sensor's resistance is lower, its decline is major but the sensor has more sensitivity. As a consequence, the operating sensor is better at 300 \({}^{\circ}\)C when the sensor's resistance is into the interval of 578 \(\Omega\) and 431 \(\Omega\).
# Real-Time Interactive Parallel Visualization of Large-Scale Flow-Field Data
## 5 Results
### Real-Time Interaction Analysis  
When visualizing large-scale flow-field data, it is necessary to provide users with real-time interactive means to support the observation of data from any angle, making the three-dimensional structured data more intuitive. However, due to the large scale of the data, the communication problem between the visualization terminal and the rendering node becomes a bottleneck for real-time interactive visualization. To solve this problem, this paper proposed a multi-level asymmetric communication management method and designed experiments to verify the effectiveness of this method. We launched 64, 128, 256, 512, and 1024 processes, respectively, to interact with two sets of steady-state data with 500 million grids and 800 million grids. Figure 12 displays the visualizations of these two sets of large-scale grid data renderings. The left mouse button is used to select all components and rotate them randomly along the X, Y, and Z axes. The middle mouse button is used to control the random movement of components in the viewport. The mouse wheel is used to slide to zoom in or out of components and view model details. We performed each of the above three interactive operations five times per group and took the average value. Table 3 shows the response times of the system during a real-time interaction. From the experimental results, it can be seen that the multi-level asymmetric communication management method proposed in this paper can reduce the real-time interaction response time to a millisecond level, meeting the needs of real-time interaction in the large-scale parallel visualization of flow fields.
## 3 Modeling Approach
### Influence of Lateral Line Stiffness of Pier Top on Natural Vibration Characteristics of Bridge  
Under the five defined pier top lateral stiffness conditions, a finite element model of the complete 10 \(\times\) 25 m simply supported beam bridge, including substructures, was established, and its modal characteristics were analyzed. The fundamental natural frequencies for lateral bending, vertical bending, and longitudinal floating modes under each condition are summarized in Table 6.  
The analysis of the chart demonstrates that with a constant vertical linear stiffness at the pier top and an increasing lateral linear stiffness from \(-50\%\) to \(+50\%\), the bridge's fundamental frequencies exhibited distinct trends: the lateral bending frequency increased progressively by 61.8% from 0.841 Hz to 1.361 Hz, while the vertical bending and longitudinal floating frequencies decreased by 2.3% from 5.343 Hz to 5.218 Hz and 29.2% from 1.109 Hz to 0.785 Hz, respectively.
## 1 Introduction
The current study stands out compared to previous studies, by designing a propulsion system based on pulsed plasma thruster technology and incorporating deployable solar panels with maneuvering capability using a Sun sensor to maximize solar flux collection efficiencies to enhance on-board power in addition to thermal design addressing thermal management and power requirements to carry out the mission to completion by deorbiting the CubeSat to a graveyard orbit. Previous studies were focused on the development and analysis of standalone subsystems such as propulsion system or deployable solar panels system.
## 2 Materials and Methods
### The Model of Atmospheric Turbulence  
In this paper, a Kolmogorov turbulence phase screen model [20; 24] is taken into account to simulate the atmospheric turbulence. Its power spectrum function can be expressed as [34; 35]:  
\[\Phi(K)=0.033C_{n}^{2}K^{-11/3} \tag{2}\]  
where \(L_{0}\) and \(l_{0}\) represent the outer turbulence radius and the inner turbulence radius respectively. \(C_{n}^{2}\), as the atmospheric refractive-index structure parameter, characterizes the intensity of atmospheric turbulence. It describes the variation of the refractive index between two points. The refractive index can also be determined from temperature and pressure. In addition, three important parameters, such as the Rytov variance \(\sigma_{R}^{2}\), the overall strength of turbulence \(D/r_{0}\) and the Fresnel number \(N_{f}=D/(\lambda L)\) are selected to emulate the atmospheric turbulence [22], where \(L\) represents the link distance, \(D\) denotes the aperture sizes of the link and \(\lambda\) is the wavelength. Atmospheric refractive index is related to the Fried coherence length \(r_{0}\) which can be determined using following equation [35]:  
\[r_{0}=\left[\frac{2.91}{6.88}k^{2}\int\limits_{0}^{L}C_{n}^{2}(z)dz\right]^{- 3/5}. \tag{3}\]Assuming that \(C_{n}^{2}(z)\) is a constant value along the transmission path, \(C_{n}^{2}(z)\equiv C_{n}^{2}\), \(r_{0}\) can be simplified as \(r_{0}=0.185\left[\lambda^{2}/\left(C_{n}^{2}Z\right)\right]^{3/5}\), where \(Z\) is the transmission distance. In numerical simulation, the long channel under atmospheric turbulence can be divided into \(N\) successive sub-channels and can be simulated by inserting random phase screen in the middle of each sub-channel. Then \(r_{0}\) can be written as \(r_{0}=0.185\left[\lambda^{2}/\left(C_{n}^{2}\times\Delta z\right)\right]^{3/5}\). To measure the scintillation, the Rytov variance \(\sigma_{R}^{2}\) is given by the following expression  
\[\sigma_{R}^{2}=1.23k^{7/6}C_{n}^{2}L^{11/6} \tag{4}\]  
In this way, a kolmogorov turbulence phase screen can be expressed as  
\[D_{\phi}(r)=6.88(r/r_{0})^{5/3} \tag{5}\]  
where \(\phi\) is assumed to be the distortion phase caused by the turbulence after the light beam transmits through the turbulence phase screen. Figure 1 shows the schematic demonstration of the atmospheric turbulence channel with \(N\) random phase screens.
## 3 Results  
### Material Balance of the Obtained Products of Steam Pyrolysis  
Figure 6 presents the average values of the mass ratio of the products yielded by the steam pyrolysis of OS in the form of semi-coke, liquid hydrocarbons, and non-condensable gas-phase products. The figure also shows the composition of non-condensable gas-phase products of steam pyrolysis.  
Figure 6 shows that the target energy product of steam pyrolysis is liquid hydrocarbons (30.4 wt%) and combstible non-condensable gas (26.5 wt%). In turn, semi-coke (39.6 wt%) shows a high content of inorganic components due to a high ash content in the original OS sample. Thus, the market product of steam pyrolysis of OS is liquid hydrocarbons, and combstible gas-phase compounds and carbon residue are considered as a secondary energy carrier to compensate for the energy costs of the setup when generating superheated steam. In turn, bitumen formed from heavy compounds of the steam-gas mixture is a direct analogue of the additive used in the manufacture and laying of asphalt [30].  
[PERSON] et al. [2] found that the yield of gas-phase products grows at increased OS pyrolysis temperatures. At 700 \({}^{\circ}\)C, the mass ratio of the obtained liquid hydrocarbons and gas-phase products was virtually 1:1. This effect is attributed to the secondary cracking of petroleum hydrocarbons [31].  
Non-condensable gas-phase products contain a large number of combustible compounds such as CH\({}_{4}\) (17.3 vol%), C\({}_{2}\)H\({}_{6}\) (8.9 vol%), and CO (33.5 vol%) (Figure 6). In this case, H\({}_{2}\) (15.9 vol%) is most likely formed due to the interaction of carbon and vapor (1), and H\({}_{2}\)S (3.3 vol%) is formed due to hydrogen and sulfur (2) contained in OS [32].  
\[\text{C}+\text{H}_{2}\text{O}\rightarrow\text{CO}+\text{H}_{2} \tag{1}\]  
\[\text{S}+\text{H}_{2}\rightarrow\text{H}_{2}\text{S} \tag{2}\]  
Figure 6: (**a**) Material balance of the products yielded by steam pyrolysis of OS and (**b**) the composition of non-condensable gas-phase (NCG) compounds.  
Partial imbalance in terms of the content of inorganic components can be due to the inhomogeneity of the OS sample used and a relatively high productivity (300 kg/h) of the experimental setup, and a large number of components in the technical system: pipelines, fittings, and containers, etc.
## 1 Introduction
### Knowledge Gap  
Despite significant advancements in InSAR, GPR, and ERT, challenges remain in achieving a fully integrated approach for subsidence detection, geohazard assessment, and infrastructure monitoring. These methods have proven effective in various studies, yet key gaps persist in their application and interpretation. For example, while recent studies have demonstrated the benefits of combining InSAR, GPR, and ERT, no standardized framework exists for their joint application. Variations in data acquisition, processing, and interpretation techniques create inconsistencies that hinder large-scale adoption. A systematic methodology that optimally integrates surface subsidence monitoring (InSAR) with high-resolution subsurface imaging (GPR and ERT) remains underdeveloped. InSAR, while effective for surface monitoring, cannot directly characterize subsurface anomalies, which are often the root cause of subsidence [7]. GPR struggles with limited penetration in con ductive environments, and ERT's resolution diminishes with increasing depth, particularly in complex geological settings [14; 25]. ERT offers deeper penetration but has lower spatial resolution at greater depths. Bridging these capabilities through an optimized data fusion approach is still a significant research challenge. Furthermore, integrating data from these methods into cohesive models is technically demanding and resource-intensive [26; 27]. The influence of dynamic environmental factors, including seasonal moisture variations and groundwater extraction, further complicates geophysical data interpretation [28].
# Hybrid Graph Models for Traffic Prediction
## 4 Methodology
### STA Module
#### 4.1.1 Temporal Attention and Spatial Attention  
In recent years, attention mechanisms have been widely used in various tasks and have achieved excellent performance. The goal of the attention mechanism is to select valuable information for the current task from all inputs. As shown in Figure 1b, historical traffic conditions can also have an impact on future road conditions, and it presents a nonlinear relationship. As shown in Figure 4, in the STA module, the proposed method employs temporal attention and spatial attention to extract nonlinear temporal dependencies and complex spatial correlations, respectively. Instead of the idea of extracting spatial-temporal information in stages, spatial-temporal attention simultaneously extracts road information to improve accuracy.  
Figure 4: Spatial–temporal attention module.  
Figure 3: Model framework.  
In the temporal dimension, the HGM uses a novel attention mechanism, which can dynamically capture long-range temporal information. The proposed method uses a multi-head attention mechanism to stabilize the training process of the proposed method. Multi-head attention can focus on information in different subspaces, which is beneficial for extracting rich spatial-temporal information.  
As shown in Figure 5, the \(k\)th attention score of node between \(t\) and \(t_{j}\) time steps can be defined as:  
\[\omega_{t,t_{j}}^{k}=\frac{<h_{t_{j},t_{j}}^{l-1}||STE_{v_{i},t_{j}},h_{v_{i},t _{j}}^{l-1}||STE_{v_{i},t_{j}}>}{\sqrt{d}_{k}}\]  
\[\beta_{t,t_{j}}^{k}=\frac{exp(\omega_{t,t_{j}}^{k})}{\sum_{t_{j}=t_{1}}^{t_{k}} exp(\omega_{t,t_{j}}^{k})}\]  
where \(||\) represents the vector concatenation operation, \(h_{v_{i},t}^{l-1}\) is the hidden layer state of the node \(v_{i}\) at time \(t\), \(<\cdot\cdot>\) denotes the inner product operator, and \(d_{k}=\frac{P}{K}\) means the output dimension of the \(k\)th attention head. Therefore, the dynamic information extracted by the node \(v_{i}\) at the \(l\)th layer through temporal attention at time \(t\) can be defined as:  
\[ht_{v_{i},t}^{l}=||_{k=1}^{K}\{\sum_{t_{j}=t_{1}}^{T_{p}}\beta_{t,t_{j}}^{k}\cdot h _{v_{i},t_{j}}^{l-1}\}\]  
It can be seen from Figure 1 that the influence relationship between nodes is changing dynamically at different time steps. Similar to temporal attention, the HGM also takes multi-head attention to extract spatial information. Spatial attention assigns different weights at different times according to the correlation between nodes. The proposed method comprehensively considers the dynamic spatial and temporal correlation, which is beneficial to improving the prediction performance.