esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Node Clustering in Cognitive Radio Wireless Sensor Networks
## 3 CR-WSN Characteristics and Architecture
### Types of CR-WSNs
#### 3.2.1 All WS Nodes Equipped with CR (CR-WSN-all)  
In this type of CR-WSN, all WS nodes are equipped with a CR module. These CR-WS nodes, also called secondary users (SUs), in the network sense available spectrum within a particular region and report to the base station (BS) or an intermediate node. The existing clustering algorithms in  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Factors** & **WSNs** & **CR-WSNs** \\ \hline \multirow{2}{*}{Channels} & Usually single channel & \multirow{2}{*}{Multiple channels are required} \\  & (multiple channels possible) & \\ \hline \multirow{2}{*}{\begin{tabular}{c} Protection of \\ PU access priority \\ \end{tabular} } & Not an issue & \multirow{2}{*}{\begin{tabular}{c} Must be protected; incumbent license \\ holder for the channels must get priority \\ \end{tabular} } \\ \hline \multirow{2}{*}{\begin{tabular}{c} Hardware \\ \end{tabular} } & Readily available (off-the-shelf) & Not readily available; still at the R\&D level \\ \hline \multirow{2}{*}{\begin{tabular}{c} Sensor form factor \\ \end{tabular} } & Small & Moderate to large \\ \hline \multirow{2}{*}{\begin{tabular}{c} Memory \\ \end{tabular} } & Limited & High \\ \hline \multirow{2}{*}{\begin{tabular}{c} CH \\ \end{tabular} } & Any node can take a CH role & \multirow{2}{*}{\begin{tabular}{c} Mostly dedicated; role changes if all WS \\ nodes have CR \\ \end{tabular} } \\ \hline \multirow{2}{*}{\begin{tabular}{c} CH as a single point \\ of failure \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} Relatively less critical \\ \end{tabular} } & Critical in case of dedicated CH \\ \hline Computation capability & Moderate & High \\ \hline Power & Power constraints & Comparatively higher power required \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Global addressing scheme \\ \end{tabular} } & Not required; not recommended & Generally not required, but recommended \\ \hline Topology change handling & Relatively easy & Difficult \\ \hline Trust and security & Relatively less critical & Highly critical \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Duty cycle \\ \end{tabular} } & Low & High; required to monitor PU arrival \\ \hline Bandwidth deficiency & Yes & Generally not an issue \\ \hline \multirow{2}{*}{Metrics} & Efficiency, resolution, latency, scalability, robustness & PU arrival prediction, channel switching, latency, robustness \\ \hline \hline \end{tabular}
\end{table}
Table 1: Clustering differences between WSNs and CR-WSNs.  
the literature consider this type of CR-WSN. Node deployment and clustering is easy in this type of network. However, it is costlier in terms of form factor and energy consumption.
## 3 Results and Discussions
### Electrochemical Analysis
The values of cathodic (\(\beta\)c) and anodic (\(\beta\)a) Tafel slopes, corrosion potential (E\({}_{\rm corr}\)), corrosion current density (\(J_{corr}\)), polarization resistance (\(R_{p}\)), and corrosion rate (\(CR\)) for carbon steel in six different solutions are presented in Table 2.  
The presence of EEA4 NB significantly decreases current densities and increases \(R_{p}\). The lowest \(J_{corr}\) (1.45 \(\times\) 10\({}^{-5}\) A\(\cdot\)cm\({}^{-2}\)) and the highest \(R_{p}\) value (1062 Ohm\(\cdot\)cm\({}^{2}\)) were recorded at a concentration of 5 \(\times\) 10\({}^{-3}\) mol\(\cdot\)L\({}^{-1}\) EEA4 NB in solution. This indicates the formation of a stable organic layer on the carbon steel surface. At higher inhibitor concentrations, an increase in adsorption and surface coverage generates a better protective layer capable of blocking the reaction sites and reducing the rate of the corrosion reaction. The E\({}_{\rm corr}\) values are shifted slightly in the presence of EEA4 NB. The \(J_{corr}\) value decreases with the addition of the inhibitor. Tafel slopes \(\beta\)a and \(\beta\)c are affected by the inhibitor, indicating that the inhibitor acts as a mixed-type one. The surface of carbon steel specimens immersed in saline solution in the presence and absence of the inhibitor was examined using SEM. In the absence of the inhibitor, the metal's surface was corroded, but in the presence of the inhibitor, the micrograph reveals a decrease in the corrosion sites due to the formation of an adsorption layer of the inhibitor on the metal surface. SEM reveals the formation of a smooth, fusiform, dense protective layer in the presence of the inhibitor (Figure 4b), compared to the uncoated bare carbon steel (Figure 4a). Based on the approximate quantitative analysis provided by EDX (Figure 4c,d), it is observed that in the carbon steel sample, Fe constitutes approximately 61%, while in the coated sample, it has decreased to around 18% due to the presence of the electrode's protective film. Nitrogen is present in the regions corresponding to C, O, and Fe peaks below 2 KeV, but the peaks generated by N are not visible in the spectrum due to their lower intensity compared to those generated by other elements. It is observed that the results of surface coverage are in good agreement with those given by the EIS method. The quantitative measure of the protective effect was calculated by comparing the values of the charge transfer resistance in the absence (\(R_{p}\)) and presence of an inhibitor (\(R_{p0}\)), as given by Equation (6) [1,20]:  
\[\text{IE (\%)}=(1-R_{p}/R_{p0})\times 100 \tag{6}\]  
where \(R_{p}\) and \(R_{p0}\) represent the polarization resistance for inhibited and uninhibited systems, respectively.
## 1 Introduction
The most important research studies are as follows: (i) [PERSON] et al. [17] applied setpoint temperatures based on the comfort limits of the simplified model from ASHRAE 55-2013 [18], varying monthly, in three residential buildings located in various cities of Spain. Energy consumption was reduced by 20% and 80% in heating and cooling, respectively; (ii) [PERSON] et al. [19] analyzed the energy saving achieved by the thermal comfort model from ISSO74 [20]. The use of the lower limit of such standard as the heating setpoint temperature achieved an energy saving of 74%; (iii) [PERSON] et al. [21] developed the Adaptive Comfort Control Implemented Model (ACCIM), which consists of a Building Energy Model (BEM) to which these adaptive setpoint temperatures are applied. Daily adaptive setpoint temperatures were used in a residential building located in the same cities on which the study by [PERSON] et al. was based [17], and energy savings between 10 and 46% were achieved according to the type of climate; (iv) [PERSON] et al. [22] observed that the application of the ACCIM, together with the use of the mixed-mode in an administrative building located in the south of Spain, achieved savings between 36.7 and 59.5% in the building energy consumption; (v) [PERSON] et al. [23] analyzed the energy savings achieved according to the three categories from the EN 15251:2007 standard [24]. The results showed that the energy saving was 31.34% and 69.91% for the categories I and III, respectively; (vi) [PERSON] et al. [25] applied adaptive setpoint temperatures based on EN 15251:2007, EN 16798-1:2019, and ASHRAE 55-2017 standards in an office building located in 65 cities; (vii) [PERSON] et al. [26] studied the energy and economic feasibility of carrying out different energy conservation measures of facades by applying adaptive setpoint temperatures. It is necessary to consider that these research studies where carried out in mild and cold climates, in which the climates of the cities of Barcelona, Rome, Berlin, Bern, and Vienna are included. The choice of the cities could be therefore justified, considering that these climates include the zones with the greatest population of Europe. This research aims to analyze the energy consumption resulting from applying the ACCIM in the current scenario, as well as in future scenarios influenced by climate change, considering not just the scenarios included in the Special Report on Emissions Scenarios (SRES), but also the Representative Concentration Pathway (RCP) scenarios, all in the year 2050. So, the saving from applying the ACCIM based on the EN16798 standard instead of the static comfort model could be assessed, as well as the variations in the consumption from the evolution of the various future climate scenarios with respect to current scenario.
###### Abstract  
Assessing the seismic vulnerability of large numbers of buildings is an expensive and time-consuming task, requiring the collection of highly complex and multifaceted data on building characteristics and the use of sophisticated computational models. This study reports on the development of a data mining technique: Support Vector Machine (SVM) for resolving such multi-dimensional data problems for assessing buildings' seismic vulnerability at a regional scale. Particularly, we developed an SVM model for rapid assessment of the macroscale seismic vulnerability of buildings in terms of spectral yield and ultimate points of their capacity curves. Two case studies, one with 11 building characteristics and the other with 20, were used to test the proposed SVM model. The results show that when 20 building characteristics are included, an individual building's seismic vulnerability in term of its spectral yield and ultimate points can be predicted by the proposed SVM model with an average 64% accuracy if the training dataset contains 400 samples, rising to 74% with 4400 training samples. Coupling the proposed technique with demand curves based on buildings' locations will enable rapid and reliable seismic-risk assessment at a regional scale, requiring only basic building characteristics rather than complex computational models.  
building seismic vulnerability; data mining; earthquake; seismic risk; Support Vector Machine
## 3 Segmentation of Nanostructured Materials for Gas Sensing
### Molybdenum Oxide (MoO\({}_{3}\))
Another key advantage of MoO\({}_{3}\)-based sensors is their stability across a wide range of operating temperatures, making them robust enough for industrial and environmental applications where temperature fluctuations are common. MoO\({}_{3}\) can operate effectively at temperatures from room temperature up to several hundred degrees Celsius, which is useful in high-temperature industrial environments. Additionally, MoO\({}_{3}\)'s high chemical stability prevents degradation under extended exposure to reactive gases, ensuring long-term operational reliability and reducing maintenance needs [102; 103; 104; 105]. The potential of MoO\({}_{3}\)-based sensors is further expanded by the use of light activation, which can reduce the need for high operating temperatures. UV or visible light activation generates electron-hole pairs that assist in gas adsorption and desorption processes, enabling sensitive detection at room temperature. This photoactivation is particularly advantageous in wearable and portable gas sensors, as it allows low-power operation without sacrificing performance. Studies have demonstrated that light-activated MoO\({}_{3}\) sensors can detect gases like NH\({}_{3}\) and NO\({}_{2}\) with high sensitivity and selectivity, making them ideal for applications requiring minimal energy consumption [102; 103]. MoO\({}_{3}\)'s high sensitivity to a broad spectrum of gases, coupled with its stability, adaptability to nanostructuring, and compatibility with doping and composite strategies, makes it a versatile material for gas sensing. Recent research has mainly focused on using MoO\({}_{3}\) for detecting ammonia, VOCs, and hydrogen sulfide in industrial surroundings. Research is mostly focused on enhancing sensitivity, selectivity, and response time. And even though MoO\({}_{3}\) is not as common as TiO\({}_{2}\) in this field, the ongoing research into light-activated and doped MoO\({}_{3}\) sensors continues to enhance its performance, making it a valuable material in the development of next-generation sensors for environmental and industrial applications, and further increasing its use (Figure 11).
## 2 Propulsion System Structure  
### Conventional Storage Tank Structure  
A propulsion system consists of a storage tank, pipeline, filter, pressure sensor, filling port, isolation valve, electromagnetic valve, thruster, etc., is shown in Figure 1. The propulsion system uses liquid ammonia as the propellant and relies on ammonia under pressure at the ordinary temperature for liquefaction. The control commands causes the thruster to open and close as ammonia is sprayed from the thruster under the action of saturated ammonia vapor. In a vacuum environment, owing to the flash evaporation characteristics of liquid ammonia, a rapid evaporation of ammonia occurs in the nozzle. Ultimately, liquid ammonia becomes a two-phase flow state to be sprayed and can generate thrust to provide the impulse for satellite orbit control.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Optimization Factors** & **Optimization Targets** \\ \hline Length (\(d\)) & Compression stress (\(\sigma\) ) \\ Three direction intervals (\(d_{x}\), \(d_{y}\), and \(d_{z}\)) & volume utilization ratio (\(v\)) \\ Wall thickness (\(T\)) & Weight (\(m\)) \\ Connecting distance to the side wall (\(D\)) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Optimization factors and targets for the storage tank.  
Figure 1: Propulsion system composition (**a**) design diagram; and (**b**) the actual propulsion system.  
Owing to the limitation of the envelope size, the storage tank is designed to be an approximate cylindrical structure with an oval cross-section. Its shape and overall structure are shown in Figure 2. The storage tank is made of Ti-6 Al-4V or TC4 (annealed), and the overall parameters of the tank are as follows: height of 66 mm, width of 130 mm, length of 112 mm, thickness of 2.1 mm, mass of 0.368 kg, and volume of 0.607 L.  
Since the installation space inside a pico-satellite is limited, the envelope occupied by the storage tank is fixed, and a conventional oval and spherically-shaped storage tank will result in a significant space loss for the same envelope. The volume utilization rate of elliptical and spherical shapes is approximately 60%. Among all of the shapes, a cuboid has the largest envelope. In order to guarantee the installation and enhance the structural performance of a storage tank in an envelope of limited space, the structure of the storage tank must be improved.
# Enhancement of English-Bengali Machine Translation Leveraging Back-Translation
## 2 Related Work
### Neural Machine Translation (NMT) Architecture
#### 2.1.4 NMT Architecture in Our Study  
For our study, we proceed with LSTM (without attention mechanism) and Transformer-based architecture, as demonstrated in Section 4. Notably, it is our baseline study as stated earlier. Moreover, we exploit the OpenNMT [67; 68; 69] toolkit to build the NMT framework for our study. OpenNMT is an open-source platform that provides various pre- and post-processing methods, as well as encoder and decoder model structures for sequence transduction tasks. By OpenNMT, we can explore the performance of customized LSTM and Transformer models in the English-Bengali translation task. Besides the selection of model structure, the training data are also critical for NMT tasks. However, as a low-resource language pair, English-Bengali translation lacks parallel data, so it is difficult to achieve a high accuracy in translation for low-resource language pairs (e.g., English-Bengali, or others). We observe that the prominent methods built on top of the baseline models can help address this issue and consequently help optimize the accuracy and quality of translations significantly [2; 74].
# Spatial Baseline Optimization for Spaceborne Multistatic SAR Tomography Systems
## 5 Experimental Verification
### Analysis of Imaging Intensity Results
At \(q=0.86\sim 1.16\), the baseline distribution tended to be uniformly distributed, and the broadening coefficient of those three imaging algorithms only varied within \(\pm 0.1\), as shown in Figure 6. Therefore, summarizing the above analysis results leads to the following conclusion: the system has stable and good imaging performance along the height direction when the baseline satisfies the weak-deviation uniform distribution. We could then use the above conclusion to design the baseline. Figure 4: Imaging performance in the height direction: PSLR, ISLR, broadening coefficient. The blue dotted line in the figure only represents the imaging result when the baseline is uniformly distributed. Figure 5: The imaging results of NDFT and CS at \(q=0.2\), \(0.6\), \(1.0\), \(1.4\), and \(1.8\).
## 3 Results
### Evaluation of Regional Seismic Response  
Seismic microzonation studies have led to improvements in the information contained in seismic design codes, providing a precise framework for risk assessment and urban planning in seismically active areas. One such improvement has involved seismic site classification methods based on the average shear wave velocity in the top 30 m (Vs30), as well as the fundamental vibration period of the soil, T, to better align seismic design codes with local geological conditions [18,19]. The multiscale and multidisciplinary approach applied has highlighted the need to consider soil nonlinearity and the depth of seismic bedrock in design codes, leading to the adoption of more realistic amplification factors for seismic design in areas with complex geology [20]. Similarly, in Chile, seismic microzonation studies have recommended incorporating detailed geophysical analyses and in situ measurements to improve the accuracy of building codes, especially in areas where soil amplification can vary significantly over short distances [21, 22, 23]. The contribution of microzonation studies to the better application of performance-based design is also widely recognized [46]. In summary, the contributions of seismic microzonation studies are directly related to improvements in design standards [47, 48, 49, 50], including in conditions of earthquake-induced hazards, such as landslides and other threats [51, 52, 53].  
Figure 12 presents the pseudoacceleration spectra obtained from the 3D model, using data from the 208 measurement points, which have been divided according to the zones  
Figure 11: Seismic acceleration record used—Maule 2010 earthquake, Rapel Station, Chile [44].  
defined in the measurement campaign. As shown in Figure 12, the seismic response does not exhibit abrupt changes across the studied area, which is consistent with the discussion in Section 3.1 regarding bedrock topography. It is important to note that, when comparing the average spectra obtained from the model with the design spectra from the standards, it is observed that in the West Zone, where the dune field with more irregular topography is located (Figure 7), the measured spectra exceed the design spectrum over a broader range of periods, with this effect even being recorded for periods greater than 1.0 s. This difference suggests greater amplification in this zone, which could have significant implications for structural design in similar areas.  
While the 3D model can accurately represent the response in areas where both the surface and bedrock relief are horizontal, in sectors with local singularities, the 3D model makes it difficult to achieve sufficient refinement to capture variations in the response due to these singularities with greater precision. For these areas where more precise analysis is required, it is suggested to conduct two-dimensional analyses of cross-sectional and longitudinal profiles. These analyses will allow for better mesh refinement and a significant reduction in computational resource consumption, thereby increasing the accuracy of the results.
# Predicting Vessel Trajectories Using ASTGCN with StemGNN-Derived Correlation Matrix
###### Abstract
Finally, by leveraging the powerful capabilities of spatiotemporal graph convolutional networks in handling complex patterns and temporal dependencies, a ship trajectory prediction model is constructed to achieve accurate ship trajectory prediction. This approach effectively utilizes the rich semantic information hidden in trajectory data and directly processes sequential data, such as trajectories, in the form of graphs, enabling precise prediction of ship navigation trajectories. This method not only improves prediction accuracy but also provides a powerful tool for understanding and interpreting ship navigation behaviors.
## 4 Calibration Comparison of Landsat 8 and Sentinel 2  
Several university teams have been acquiring Landsat 8 and Sentinel 2 data since the launch of each instrument for the purposes of vicarious calibration [17; 18; 19]. Additionally, two groups have developed absolute calibration models for PICS that can be used for both OLI and MSI. Initial work comparing Landsat-8 OLI and Sentinel-2A MSI follows; more details on the study can be found in [PERSON] et al. [20].  
Because the two instruments have significant overlap in spectral bandpasses (see Figure 7), these differences must be accounted for through use of a spectral band adjustment factor (SBAF; [21]). The SBAF is target specific and requires a source of hyperspectral image data to calculate. Using the SBAF, the MSI reflectances can be converted to OLI equivalent reflectances.
## 5 Computational Implementation and Results  
The solution of the MINLP model (1)-(6) for the optimal siting and sizing of D-STATCOMs in electric distribution networks using the DCVSA was implemented in the MATLAB version 2020\(b\) on a PC with an AMD Ryzen 7 3700 2.3-GHz processor and 16.0 GB RAM, running on a 64-bit version of Microsoft Windows 10 Single language. In addition, to compare the efficiency and robustness of the proposed optimization approach the MINLP model was also solved in the GAMS software with BONMIN and COUENNE solvers [9]. The parameterization of the DVSA was taken from [8] and it is presented in Table 6.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Period** & **Act. (pu)** & **React. (pu)** & **Period** & **Act. (pu)** & **React. (pu)** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 4: Behavior of the daily active and reactive power demands.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Par.** & **Value** & **Unit** & **Par.** & **Value** & **Unit** \\ \hline \(\mathrm{C}_{\mathrm{kWh}}\) & 0.1390 & \(\mathrm{US\$kWh}\) & \(T\) & 365 & Days \\ \(\Delta_{h}\) & 0.50 & h & \(\alpha\) & 0.30 & \(\mathrm{US\$/MVAr^{3}$}\) \\ \(\beta\) & \(-305.10\) & \(\mathrm{US\$/MVAr^{2}$}\) & \(\gamma\) & 127380 & \(\mathrm{US\$/MVAr}\) \\ \(k_{1}\) & 6/2190 & \(1/\mathrm{Days}\) & \(k_{2}\) & 10 & Years \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parametrization of the objective function.