esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Simulation of Plasma Sheath Fluid Model
### Numerical Simulation Results  
In the fluid domain setup, the fluid domain space around the vehicle is appropriately enlarged with the aim of minimizing external disturbances during simulation calculations. The fluid domain around the vehicle is set up using the O-Block setup scheme, and the mesh is a structured advective mesh with 22 final setup regions; 98,123 total meshes; and 117,000 total nodes. For hypersonic fluid calculations, structured meshes give more accurate simulation results. During the flow process, there is a large gradient in the flow field area on the aircraft wall. In order to better capture the physical characteristics of the flow, the mesh points must fall into the actual boundary layer, and the mesh on the boundary layer should be encrypted. In this paper, exponential encryption of the vehicle wall along the flow direction is used for the 3D model, and since the head of the vehicle is the region where the shock waves are generated, it should also be encrypted with a mesh.  
For the numerical method of fluid mesh space, the Roe approximate Riemannian solution method is set. For mixing/chemical reactions, only the Roe format is currently available. During the solving process, the displayed format has less dissipation than the implicit format for time accuracy issues. Therefore, this simulation uses the multi-step Runge-Kutta format to solve the discrete equation. During the iterative calculation process, convergence of the calculation occurs when the residual values of each physical variable have reached the convergence criteria. Furthermore, the CFD time step should be chosen in accordance with the CFL condition (Courant Friedrichs Lewy) [21]. The convergence criterion set for this simulation experiment is that the simulation calculation can be considered to converge when the residual values of all the variables are reduced to less than \(10^{-6}\). Figure 2 contains the mesh distribution of the vehicle and the surrounding area, as well as a cloud view of the distribution of each fluid parameter.  
Figure 2: Legend of the flow field simulation. Schematic diagram of (**a**) the computational fluid region meshes, (**b**) the Mach number distribution in the fluid region, (**c**) the density distribution in fluid region, (**d**) the mass fraction distribution of N atoms in the fluid region, (**e**) the mass fraction distribution of O atoms in the fluid region, (**f**) the mass fraction distribution of NO atoms in the fluid region, (**g**) the temperature distribution in the fluid region, and (**h**) the mass fraction distribution of the electrons in the fluid region.  
To verify the correctness of the calculation results, the calculation results of the peak electron number density of the flow field along the blunt cone streamline direction of the vehicle are compared with those in ref. [30]. As shown in Figure 3, it can be seen that the simulation results are in good agreement with [30].
# On the Energy Efficiency of On-Off Keying Transmitters with Two Distinct Types of Batteries
## 3 Derivation of the BECPB for Rayleigh Channel  
This section will derive BECPB for the OOK transmitter to guarantee a prescribed SER when a noncoherent detector is used at a receiver, and a Rayleigh fading channel is assumed between the transmitter and the receiver.
###### Abstract  
Land cover classification of built-up and bare land areas in arid or semi-arid regions from multi-spectral optical images is not simple, due to the similarity of the spectral characteristics of the ground and building materials. However, synthetic aperture radar (SAR) images could overcome this issue because of the backscattering dependency on the material and the geometry of different surface objects. Therefore, in this paper, dual-polarized data from ALOS-2 PALSAR-2 (HH, HV) and Sentinel-1 C-SAR (VV, VH) were used to classify the land cover of Tehran city, Iran, which has grown rapidly in recent years. In addition, texture analysis was adopted to improve the land cover classification accuracy. In total, eight texture measures were calculated from SAR data. Then, principal component analysis was applied, and the first three components were selected for combination with the backscattering polarized images. Additionally, two supervised classification algorithms, support vector machine and maximum likelihood, were used to detect bare land, vegetation, and three different built-up classes. The results indicate that land cover classification obtained from backscatter values has better performance than that obtained from optical images. Furthermore, the layer stacking of texture features and backscatter values significantly increases the overall accuracy.  
Keywords:land cover; supervised classification; texture measures; synthetic aperture radar (SAR) imagery; support vector machine; maximum likelihood; Tehran +
Footnote †: journal: Applied Sciences
## 3 Base Model
#### Key Considerations  
* **Player behavior assumptions** : The simulation assumed player connection and disconnection behavior that may not fully capture the complexity and variability of real user behavior in online games.
* **Fog deployment scenarios**: Random allocation of fog servers might not adequately represent realistic implementation scenarios. In practical settings, the strategic placement of fog servers could significantly impact system performance.
* **Network latency consideration**: The simulation did not explicitly address network latency, which is crucial in online gaming. The location of fog servers and the quality of the connection can significantly influence the player's experience.
* **Realistic environment modeling**: Expanding the model to include more realistic features of the gaming environment, such as player geography.
* **Evaluation of fog deployment strategies**: Analyzing different fog server deployment strategies, considering factors like geographic location, processing capacity, and specific demands of the game cluster.
# Smart Inverters for Microgrid Applications: A Review
## 2 Smart Inverters
### Plug and Play  
For an inverter to be able to present this feature, it needs to be compatible with the standard communication protocols that control the microgrid. As mentioned before, the modern hierarchical control of microgrids still requires communication between different components of the grid even if they are droop based. This means that a reliable long-range communication protocol is required. In [35], the different communication technologies for smart grids has been introduced accompanied by a discussion on their advantages and disadvantages. The benefits and drawbacks of wireless  
Figure 1: Indicators of smartness for smart inverters.  
connection for smart grids are presented comprehensively in [36]. Generally, the major communication requirements for a smart grid or microgrid have been presented [35; 37; 38] as follows:  
* Data rate: This is one of the most important requirements, as not all of the communication structures are able to provide the required data rate. For example for a home or industrial area network application (HAN and IAN) the required data rate is less than 100 kbps. In contrast to a wide area application (WAN) where this figure can rise to more than 10 Mbps [39].
* Range: A microgrid consists of several DGs, energy storage systems (ESSs) and consumers. These are all interfaced to the microgrid bus by inverters that need to be able to communicate. Depending on the size of the microgrid the distance between these points can be noticeable (in the scale of tens of kilometers or more) so no all communication technologies are able to fulfill this requirement.
* Security: If the communication structure is not secure enough [40], the whole system will be vulnerable to both physical and cyber-attacks. This is a challenging aspect regarding some natural characteristics of a smart grid. For example, the smart grid is very sensitive to latency and implementing conventional security measures might introduce new and more delays [41].
* Latency: Depending on the function of the device in a smart grid, the allowed threshold for the latency is different. In the concept of inverter control signals, this allowance is less than ten milliseconds.
* Reliability and Scalability.  
In order to nominate a communication protocol for smart grids and subsequently smart inverters, the aspects addressed above should be considered carefully and the differences between machine-to-machine (M2M) communication and human-to-human (H2H) communication should be understood. Both wired and wireless communication technologies can be implemented for smart inverters.
## 3 Measurement Setup
### Measurement Methods  
The signal strength information can be captured by extracting RSSI from received packets. Based on the conversion relation, the received power can be estimated. According to the typical WSN applications, three different antenna heights were selected (5 cm, 50 cm and 1 m, respectively), among which the 5 cm antenna height represented the situation that sensor nodes were directly thrown to the ground. The transmitter was fixed at one position and the receiver was initially placed at 1 m away from the transmitter. The RSSI was first collected every meter at a distance of up to 10 m from the transmitter, then every 2 m at a distance of up to 20 m, and finally every 5 m until a distance up to 50 m. Hence, there were 21 test points in each measurement. At each test point, 200 packets were collected, providing an adequately large dataset for estimating the statistical properties of path loss. The eventual RSSI value at each test point was an average of the 200 RSSI samples. The same methodology was adopted for each of the above-mentioned scenarios.  
We assumed that, in these scenarios, the radio channel was quasi-static. Furthermore, to ensure a fair comparison, the measurement devices were kept stationary by not allowing any moving objects surrounding the test site. In addition, we took the measurement under the same weather condition.
## 4 Research Gaps  
Available H\({}_{2}\) storage methods have both advantages and disadvantages [98, 99, 100, 101, 102]. The best storage option can be determined by several criteria, including cost, safety, storage capacity and integrity, weight (i.e., the amount of hydrogen that can be stored compared to the mass of the storage system), and use-case requirements. Barriers to UHS include the following challenges. (a) Geological challenges: ensuring the suitability and stability of geological formations for storage, addressing potential leakage pathways, and managing impacts on the surrounding environment. (b) Technical challenges: maintaining well integrity, addressing H\({}_{2}\) emissions and corrosion, and managing technical tightness to  
Figure 11: Comparison of working storage resources in Lithuania, Latvia, and Poland [95].  
prevent leaks. (c) Geochemical reactions: managing geochemical reactions that can lead to H\({}_{2}\) loss and the production of unwanted gasses from subsurface.  
UHS in Lithuanian geological settings presents several research gaps that need addressing to ensure its feasibility and effectiveness. Key gaps include the detailed mapping and characterization of potential storage sites, understanding H\({}_{2}\)-reservoir interactions, and assessing seismic and structural integrity risks. Technical gaps involve optimizing injection and withdrawal mechanisms and improving H\({}_{2}\) migration containment and monitoring technologies. Environmental research should focus on comprehensive impact assessments, potential groundwater and ecosystem risks, and mitigation strategies for induced seismicity. Integrating UHS with renewable energy systems, developing smart grid technologies, and exploring innovative storage technologies are essential for enhancing efficiency and safety. Addressing these gaps requires a multidisciplinary approach involving geologists, engineers, environmental scientists, economists, and policymakers, with collaboration between academia, industry, and government to advance UHS implementation in Lithuania.
## 2 Study Area and Spatial Data Set
### Study Area
The topographical data was obtained through digitizing using aerial photographs taken in 2006; additional corrections were performed and updated by other high-resolution satellite images. The satellite image used for correction was Pleiades 1A, spatial resolution of multi-spectral is 0.5 m, and the image was similar to that of the aerial photograph. Land cover maps were classified into 8 main categories using an unsupervised classification method from aerial photographs with a spatial resolution of 0.25 m taken in 2013. In addition, Kompsat-3 remote sensing image with spatial resolution of 0.7 m was used to evaluate the classification accuracy [45]. The digital elevation model (DEM) was generated from a topographic map with a resolution of 30 m using a 1:5000 digital topographic map from the National Geographic Information Institute (NGII). The slope gradient, plan curvature, relative slope position, valley depth, LS factor, convergence index, drainage basin, TWI, and TRI were calculated using the DEM [54]. Also, various thematic maps such as those depicting soil texture, land cover, and hydrogeology were resampling at 30 m resolution and used in this study [11]. Various parameters were used to analyze the GPP with more precision. The LS factor is the ratio of soil loss per unit catchment area to the slope length (L) and slope steepness (S). The formula proposed by [PERSON] and [PERSON] [55] for calculating the LS factor is as follows (Equation (4)):  
\[LS=\left(\frac{A_{s}}{22.13}\right)^{0.6}\left(\frac{\sin\beta}{0.0896}\right) ^{1.3} \tag{4}\]  
where,  
\(A_{s}\) is the catchment area  
\(\beta\) represents the slope gradient measured in degrees  
The convergence index represents the structure of the slope as a set of convergence and divergence sites. The index value for maximum convergence is +100. Conversely, the index value for maximum divergence is \(-\)100. If there is a flat, the index is 0 [56]. This means that the index value is closer to convergence (+100) for larger slope values. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Type of Aquifers**} & \multicolumn{4}{c}{**SPC (m\({}^{3}\)/day/m)**} & \multicolumn{4}{c}{**T (m\({}^{2}\)/day)**} \\ \cline{2-9}  & **Min** & **Max** & **Average** & **Median** & **Min** & **Max** & **Average** & **Median** \\ \hline Porous rock & 2.23 & 769.23 & 20.07 & & 0.70 & 489.91 & 23.78 & \\ saturated aquifers & 2.67 & 283.33 & 37.60 & & 0.83 & 73.16 & 11.30 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The results of the pumping test of wells in the Okcheon-gun. Figure 2: _Cont._  
the center cell and the eight cells surrounding it. This difference corresponds to the average altitude change between any point on the grid and the surrounding area.
# Shear-Wave Velocity Model from Site Amplification Using Microtremors on Jeju Island
## 4 Results
### HALB Site
The first substantial negative RC peak of \(-0.20\), at depths of 80-100 m, denotes the transition from the surface outcrop to the LVL, marked by a considerable velocity reduction of 400 m/s, extending to depths of 350-370 m. Beyond this depth, three velocity increments (0.20, 0.14, and 0.22) were encountered, culminating in the deep basement at depths of 690-710 m. The depth, sign, and magnitude of the RC aligned well with the transitions, velocity jumps, and LVL. Figure 7: Estimated shear-wave velocity (Vs) profile from the inversion of HALB site; thin lines indicate all the inversion results, and thick gray line indicates the optimal Vs profile. Therefore, the final velocity model for the HALB site was interpreted as comprising five layers. This interpretation was derived by holistically considering both the frequently mapped models, as indicated by their relatively darker color, and the average RC values, as depicted in Figure 7. Compared to the GOS site in the same western region, notable differences were observed in the upper boundary depth of the LVL at 230-250 m (GOS) versus 350-370 m (HALB), although the lower boundary depths were similar at 390-410 m (GOS) and 350-370 m (HALB). Additionally, the commencement depth of the basement was nearly identical at both sites.
###### Abstract  
Wireless sensor networks (WSN) are deployed for many applications such as tracking and monitoring of endangered species, military applications, etc. which require anonymity of the origin, known as Source Location Privacy (SLP). The aim in SLP is to prevent unauthorized observers from tracing the source of a real event by analyzing the traffic in the network. Previous approaches to SLP such as Fortified Anonymous Communication Protocol (FACP) employ transmission of real or fake packets in every time slot, which is inefficient. To overcome this shortcoming, we developed three different techniques presented in this paper. Dummy Uniform Distribution (DUD), Dummy Adaptive Distribution (DAD) and Controlled Dummy Adaptive Distribution (CAD) were developed to overcome the anonymity problem against a global adversary (which has the capability of analyzing and monitoring the entire network). Most of the current techniques try to prevent the adversary from perceiving the location and time of the real event whereas our proposed techniques confuse the adversary about the existence of the real event by introducing low rate fake messages, which subsequently lead to location and time privacy. Simulation results demonstrate that the proposed techniques provide reasonable delivery ratio, delay, and overhead of a real event's packets while keeping a high level of anonymity. Three different analysis models are conducted to verify the performance of our techniques. A visualization of the simulation data is performed to confirm anonymity. Further, neural network models are developed to ensure that the introduced techniques preserve SLP. Finally, a steganography model based on probability is implemented to prove the anonymity of the techniques.  
wireless sensor networks; anonymity; context privacy; global adversary +
Footnote †: journal: Sensors
# Improved Calculation Method for Siphon Drainage with Extended Horizontal Sections
###### Abstract  
Slope siphon drainage is a convenient and efficient above-ground drainage method that is free of manual power and can effectively maintain the stability of potential landslides and prevent the loss of life and property. The complex engineering topography inevitably requires the use of siphon drains with a total length of more than 150 m and a horizontal section length of more than 80 m, which significantly increases the difficulty of calculating the drainage capacity and thus affects the actual utilization of the project. The traditional siphon flow rate equation does not apply to long-pipe siphon conditions, especially when the lift is close to the limit, and there are significant errors in the calculation results, for which we propose a new calculation method. The proposed method considers both air release and flow-pattern classification. Thirty-six sets of experiments were conducted to validate our proposed calculation method. The results showed that our method not only calculated the siphon flow velocity well but also predicted the main flow pattern in the siphon in the experiment well. Furthermore, the equation for calculating the siphon flow velocity was extended to the siphon operation mode with long horizontal sections.  
siphon drainage; flow rate; slug flow; pressure drop; extended horizontal section +
Footnote †: journal: Applied Sciences
# Rolling Bearing Fault Diagnosis Based on CEEMDAN and CNN-SVM
## 3 CEEMDAN and CNN-SVM Fault Diagnosis Models
### Fault Diagnosis Process  
The flowchart of the rolling bearing fault diagnosis method proposed in this paper is shown in Figure 3, and its specific steps are as follows:  
1. Signal decomposition using CEEMDAN algorithm. The original vibration signal is decomposed into multiple IMF components using the CEEMDAN algorithm, each with different frequency characteristics.
2. Filter and reconstruct the signal. By calculating the correlation coefficient of each IMF component, screen the IMF components and reconstruct the signal to remove noise interference.
3. Signal transformation and division of the data set. The reconstructed one-dimensional vibration signal is converted into a two-dimensional grey-scale map, and the training set, validation set and test set are divided according to the ratio of 3:1:1.
4. Feature extraction using CNN. CNN extracts feature vectors from the 2D grey-scale map which will be used for subsequent fault classification.
5. Training of GWO-SVM classifier. Optimise the parameters of SVM using GWO to retrieve the SVM classifier with optimal parameters.
6. Perform fault classification and output results. Input the feature vectors of the test set into the SVM classifier with optimal parameters, perform classification and output the fault classification results.
# Exploring Multi-Agent Debate for Zero-Shot Stance Detection: A Novel Approach
## 4 Experiment
### Dataset and Evaluation Metrics  
(1) SemEval-2016. SemEval-2016 Task 6 is a widely utilized benchmark dataset for stance detection in social media text. The objective of this dataset is to identify the stance expressed in a given tweet toward a specific target. The dataset comprises 4870 tweets, covering five distinct targets: [PERSON] (HC), feminist movement (FM), legalization of abortion (LA), atheism (A), and climate change is a real concern (CC). Each tweet is annotated with one of three stances: Favor, against, or neutral. This task is formulated as a multi-class classification problem, aiming to determine whether the attitude expressed in a tweet toward its target is supportive, opposing, or neutral. The dataset is designed to provide a diverse range of stance expressions, facilitating the training of stance detection models, particularly in the context of social media, where text is typically brief, informal, and may contain sarcasm or ambiguous phrasing.  
(2) P-Stance. The P-Stance dataset is primarily designed for stance detection concerning political targets, specifically focusing on social media text related to the 2020 U.S. presidential election. This dataset comprises 21,574 tweets and covers three political figures: [PERSON] (DT), [PERSON] (JB), and [PERSON] (BS). It is predominantly structured as a binary classification task, with the objective of predicting whether a tweet expresses support for or opposition to these political figures. Compared to other stance detection datasets, P-Stance presents unique challenges due to the more subtle nature of expressions in the political domain. Tweets often feature indirect references to political figures, necessitating nuanced contextual analysis to discern stances. This may involve identifying oblique references or broader political discourse associated with these individuals. Consequently, the P-Stance dataset is particularly valuable for training stance detection models capable of handling implicit expressions and subtle sentiments.  
To ensure fairness in evaluation, we adopt the micro-average F1 score as our primary evaluation metric, as recommended by the creators of the datasets. The specific calculation method for this evaluation metric is as follows:  
\[F_{\text{avg}}=\frac{F_{favor}+F_{against}}{2} \tag{10}\]  
The calculation methods for \(F_{favor}\) and \(F_{against}\) are as follows:  
\[F_{favor}=\frac{2P_{favor}R_{favor}}{P_{favor}+R_{favor}} \tag{11}\]  
\[F_{against}=\frac{2P_{against}R_{against}}{P_{against}+R_{against}} \tag{12}\]  
where \(P\) and \(R\) represent precision and recall, respectively.