esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study on Aerodynamic and Structural Design of Fairing Using Glass Fabric Composite Structure
## 4 Structural Design
### Design of Uniform Thickness for Composite Laminate  
The fairing is supported by the cab part of commercial vehicles. The maximum load was considered for structural safety, and it used 3519 Pa, the maximum pressure that was the aerodynamic analysis result. On the bottom of the fairing, positive pressure was produced in the direction perpendicular to the tangential plane on the basis of the front of the fairing, and on the top, negative pressure was generated in the direction perpendicular to the tangential plane; however, negative pressure is greater than positive one, so its structural design was performed with the distributed load of 3519 Pa which was the maximum of negative pressure. The GFRP woven fabric was used for the materials of the fairing, and it was designed to satisfy the safety factor of 3 against compressive stress. The final thickness of the structures was derived by using the equation below [16].  
\[F=P\times A \tag{12}\]  
In Equation (12), \(F\), \(P\), and \(A\) are the load value, the maximum pressure, which is the aerodynamic analysis result, and the projected area to the front of the fairing, respectively.  
\[w=F/a \tag{13}\]  
In Equation (13), \(w\) and \(a\) are the distributed load and the length of the top member of the fairing, respectively.  
\[M_{max}=\frac{1}{8}wa^{2} \tag{14}\]  
In Equation (14), \(M_{max}\) is the bending moment of the top member of the fairing.  
\[S_{min}=\frac{M_{max}}{\sigma_{all}} \tag{15}\]  
In Equation (15), \(S_{min}\) and \(\sigma_{all}\) are the allowable section modulus and allowable stress, respectively.  
\[S_{min}=\frac{1}{6}bt^{2} \tag{16}\]  
In Equation (16), \(b\) and \(t\) are the curved length and thickness of the fairing, respectively.  
The uniform thickness lamination method for composite was applied for structural design. The distributed load was applied to the laminate structure. Table 6 is the structural design specification required at the uniform thickness composite lamination method stage. As a result of structural design, the thickness and mass were 12.2 mm and 125 kg.
## 1 Introduction  
Epilepsy as a disease can be described as a group of serious brain disorders affecting people of all ages. It is characterized by the onset of spontaneous convulsive and non-convulsive seizures. Epilepsy affects approximately 50 million people with a high prevalence in the first decade of life and in individuals more than 60 years old [1,2]. Conventional antiepileptic agents (_i.e._, phenytoin and carbamazepine) and recent antiepileptic drugs (_i.e._, gabapentin, vigabatrin, remacemide, and loreclezole) are clinically effective for different seizure types. However, only 50% of sufferers are adequately treated with the currently available antiepileptic drugs (AEDs), and a few AEDs have severe side effects [3, 4, 5, 6, 7]. Therefore, the development of safer and more effective AEDs is necessary. The rational design and discovery of new antiepiletics are difficult due to incomplete data concerning the molecular modeling pathway of human epilepsy and the sophisticated mechanism of action for the majority of antiepileptic drugs [8]. Therefore, a ligand-based approach is one of the most important strategies for the development of new AEDs using the available biological information from well known and novel drugs. (2_E_)-2-[3-(1_H_-imidazol-1-yl)-1-phenylpropylidene]-_N_-phenylhydrazine-carboxamide ((2_E_)-HIPC) is a hybrid structure incorporating both imidazole and arylsemicarbazone moieties that act as pharmacophore scaffolds in some candidate anticonvulsants [9,10]. This compound has exhibited anticonvulsant activity with 16% seizure protection at a dose level of 750 umol/kg in a subcutaneous pentylenetetrazole (scPTZ) screen with no neurotoxicity [11].  
The molecular structure of (2_E_)-HIPC has not previously been explored using computational methods. Therefore, we used _ab initio_ Hartree-Fock (HF) and density functional theory (DFT/B3 LYP) approaches to model the molecular structure of (2_E_)-HIPC. DFT/B3 LYP/6-311++G(d,p) is a hybrid DFT standard computational method that gave sufficiently accurate results for organic molecules without consuming significant amounts of computing resources. Fourier transform infrared (FT-IR) and Raman spectra were also collected, and the data were compared with the computed spectra. The molecular electrostatic potential (MEP) and frontier molecular orbitals (HOMO-LUMO) of (2_E_)-HIPC were computed to predict the structural changes and reactive sites. In addition, the NMR(\({}^{13}\)C and \({}^{1}\)H) chemical shifts of the title molecule were computed by the gauge-including atomic orbital method using B3 LYP/6-311++G(d,p) calculations, and the theoretical results were compared with the experimental values.
## 5 Evaluation
### High Availability and Load Balancing  
Seven hosts (machines) are used for deploying the clustered prototype of the IIP, where one is utilized as a proxy server for load balancing requests from both information providers and consumers, two are used for deploying the main applications that handle requests forwarded (load balanced) by the proxy/load balancer host, another two are employed for data and SQL nodes of the MySQL Cluster that act as the main storage for the IIP and the last two are utilized for management nodes of the MySQL Cluster. This set-up conforms with Figure 9 and can be considered as the minimum requirement for IIP's high availability, where all nodes have exactly one redundant backup (except the load balancer). New nodes can be added when deemed needed. A test was conducted to review the general functionality of the load balancing between the redundant nodes being deployed. A test client application was developed, playing the role of information provider that keeps sending one publication message per second to one of its information channels through the load balancer host. Initially, both application servers' hosts are up and running, and the load balancer host distributes the requests evenly to both application servers. As can be seen in Figure 16a, the load balancer host (acting as a proxy) successfully forwards all requests evenly to both application servers' hosts (_i.e._, 53 and 52 requests for _worker1_and _worker2_, respectively). One of the two application servers' hosts was then turned off during the test, leaving only one application server available for handling all requests. From Figure 16b it can be seen that the load balancer host forwards all requests to the available application server's host (_worker2_), and _worker1_'s state was changed to error. From this test, it can be concluded that the redundancy of the application nodes works well for providing highly available service, while enabling new nodes to be added (horizontal scaling) and load balanced to serve incoming requests.  
In the data layer, synchronous replication among data nodes is handled automatically by the MySQL Cluster, which is monitored and managed by the management nodes. Figure 17a depicts a snapshot of a MySQL Cluster management client that describes the most current configuration of the cluster and the status of each node. In this set-up, any data committed from the service layer is inserted into both data nodes synchronously. One of the two data and SQL nodes' hosts (with IP address 192.168.1.7) was shut down. The MySQL Cluster then saves all data and writes to only one existing data node, as shown in Figure 17b. When the host is up and running again, MySQL Cluster automatically synchronizes all changes to the newly running data node.  
Figure 16: Snapshot of mod_jk status worker: (**a**) Both application nodes are up and running; (**b**) One application node (_worker1_) is down.
# Utilizing Sentinel-2 Satellite Imagery for LULC and NDVI Change Dynamics for Celephu, Bhutan
## 3 Results and Discussions
### NDVI Change Detection and Statistics  
Analysis of the data for NDVI change detection between 2016 and 2023 (Table 3) shows significant shifts in land cover types within the study area (Figure 11). There was a significant decrease in water bodies: the area decreased from \(0.96\) to \(0.06\) km\({}^{2}\) and the percentage of water cover fell from \(1.76\%\) to \(0.11\%\). This \(1.65\%\) decrease in water bodies was mainly observed due to a change in the watercourse outside the study area zone. The  
Figure 10: NDVI maps for Gelephu. (**a**) 2016 NDVI, (**b**) 2023 NDVI.  
category of \"No vegetation\" showed the most significant increase in both absolute area and percent coverage. The area of no vegetation extended from 2.87 to 8.08 km2, with the percent coverage increasing from 5.27% to 14.83%. This significant growth of 9.56% suggests a loss of vegetation cover and raises concerns about potential ecological impacts, such as soil erosion and reduced biodiversity. The class of \"Low vegetation\" also accords with substantial growth, with the area increasing from 2.07 to 24.54 km2 and the percent coverage rising from 3.80% to 45.04%. This notable expansion of 41.24% indicates vegetation changes, probably due to natural or human-induced factors such as afforestation or agricultural activities. The class of \"Healthy vegetation\" exhibited moderate growth, with the area increasing from 7.13 to 21.27 km2, and the percent coverage expanding from 13.09% to 39.04%. This positive trend of 25.95% suggests an increase in vegetation density and health within the study area. Surprisingly, the class of \"Very healthy vegetation\" experienced a significant decline. The area of very healthy vegetation decreased dramatically from 41.46 to 0.53 km2, and the percent coverage declined from 76.09% to 0.98%. This significant 75.11% decline in very healthy vegetation raises questions about possible environmental degradation, such as deforestation or land-use changes.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{2}{c}{**Area (km2)**} & \multicolumn{2}{c}{**\% Coverage**} & \multicolumn{2}{c}{**\% Change**} \\ \cline{2-6}  & **2016** & **2023** & **2016** & **2023** & \\ \hline Waterbody & 0.96 & 0.06 & 1.76 & 0.11 & \(-\)1.65 \\ \hline No vegetation & 2.87 & 8.08 & 5.27 & 14.83 & 9.56 \\ \hline Low vegetation & 2.07 & 24.54 & 3.80 & 45.04 & 41.24 \\ \hline Healthy vegetation & 7.13 & 21.27 & 13.09 & 39.04 & 25.95 \\ \hline Very healthy vegetation & 41.46 & 0.53 & 76.09 & 0.98 & \(-\)75.11 \\ \hline Total & 54.50 & 54.50 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: NDVI change detection statistics for Gelephu.  
Figure 11: Reclassified NDVI maps; (**a**) 2016 NDVI, (**b**) 2023 NDVI.
# Fuzzy-Based Dynamic Time Slot Allocation for Wireless Body Area Networks
## 4 Tree Formation and Cost Function Evaluation
### Link Cost Function for Next-Hop Selection  
The objective of the link cost function is to select a node with minimum link cost as the best parent node. It [40] is based on the parameters such as: residual energy, queue size, link reliability, the distance between the nodes and the available bandwidth. Consider the variable \(x\), where x is given as:  
Figure 2: Block diagram of a fog-based WBAN.  
\[x=w_{1}\times\frac{e_{r}}{e_{i}}+w_{2}\times\frac{q_{a}}{q_{i}}+w_{3}\times R_{i, j}(n)+w_{4}\times\frac{d}{c}+w_{5}\times\frac{b_{r}}{b_{a}}, \tag{1}\]  
where, \(e_{r}\), \(e_{i}\), \(q_{i}\) and \(q_{a}\) are residual energy, initial energy, initial queue size, and current available queue size of node j respectively. \(R_{ij}(n)\) is the current round link reliability between the nodes \(i\) and \(j\), which is estimated from Equation (3). The metrics \(d\), \(c\), \(b_{a}\), and \(b_{r}\) represent the distance between the two nodes, the coverage of a node, the required bandwidth, and the residual bandwidth respectively. \(w_{1}\), \(w_{2}\), \(w_{3}\), \(w_{4}\), and \(w_{5}\) are the weighted coefficients, where  
\[\sum_{i=1}^{5}W_{i}=1 \tag{2}\]  
The link reliability between any two sensor nodes (\(R_{ij}\)) is estimated from the exponentially weighted moving average, which is given as follows:  
\[R_{i,j}(n)=(1-\gamma)\times Ri,j(n-1)+\gamma\times\frac{N_{t}}{\tau_{tr}}, \tag{3}\]  
where \(N_{t}\) is the total number of successful packet transmission attempts through the link between the nodes \(i\) and \(j\), \(n\) is the index number of the round, \(\tau_{tr}\) is the total number of successful transmission and re-transmission attempts of all data packets, and \(\gamma\) is the average weighting factor.  
The distance between the two nodes can be calculated using Equation (4):  
\[d^{2}=(x_{1}-x_{2})^{2}+(y_{1}-y_{2})^{2}, \tag{4}\]  
where \(x\) and \(y\) are the coordinates.  
The expression for the link cost function is expressed as:  
\[LC_{i,j}=e^{-x}, \tag{5}\]  
where, \(x\) is expressed in Equation (1). The range of link cost function is (0.367,1). The mentioned link cost considered five factors in order to enhance the QoS performance of the network. The energy metric aims to stabilize the energy between the nodes, queue size metric attempts to reduce the queuing delay, the link reliability improves the reliability of the network, node coverage and the distance between the nodes are used to decrease the number of re-transmission attempts, and the residual bandwidth increases the packet delivery ratio of the network by utilizing the available bandwidth resource of the network.
# GPS-Based Indoor/Outdoor Detection Scheme Using Machine Learning Techniques
## 4 I/O Detection Scheme
### Data Collection  
We used a mobile application to collect and preprocess the raw GPS signal information. This application collects information from GPS sensors, rearranges the information, and analyses it for better input data. The mobile application was selected for data collection and preprocessing rather than a GPS sensor module because it is simple, suitable for mobile devices, and provides stable input data. In Figure 5, the number and the magnitude of GPS signals in orbit is determined and preprocessed for easier access for data collection. The satellite ID is given below each GPS signal; however, the ID is ignored during the data collection phase. The magnitude of all GPS signals will be recorded and used as input data for the machine learning classifier.  
In this study, we analyzed statistical differences by focusing on the difference between GPS signals received in indoor and outdoor environments. A large dataset comprising real signal values collected  
Figure 3: GPS sensors can receive signals when the receiver is close to an external door.  
at multiple locations and in various environments was used. We investigated the impact of indoor, semi-indoor, and outdoor environments on the received GPS signal.
# Data-Driven Damage Classification Using Guided Waves in Pipe Structures
## 2 Mode Selection, Excitation, and Reception of UGW in Pipe  
In pipe structures, there are three kinds of modes along the axial direction, which are, respectively, the longitudinal modes L(0, \(n\)), the torsional modes T(0, \(n\)), and the flexural modes F(\(m\), \(n\)), where \(m\) and \(n\) are integers greater than zero, and represent the circumferential order and the radial order. The longitudinal modes that simultaneously contain the axial and radial displacements can be analogous to the Lamb wave in the plate, and correspondingly, the torsional modes only containing the circumferential displacement are similar to the SH wave in plate-like structures.  
Figure 1 shows the group velocity dispersion curves of an aluminum pipe with an outside diameter of 102 mm and a thickness of 1.5 mm. It can be seen that, multiple modes exist at any frequency point, and at least two modes exist in a low frequency range. As the frequency increases, more modes arise. In Figure 1, there are only the first three modes displayed. It can be seen that the difference in the velocity of modes in the same group becomes smaller as the frequency increases, thus resulting in difficult separation for different modes in a high-frequency range.  
From Figure 1, it is easily concluded that all the modes are more or less dispersive in any frequency range except the basic torsional mode T(0,1), which is quite appropriate for damage detection in the pipe because of the small waveform distortion. Moreover, the guided wave of the torsional mode only including shear strain \(\varepsilon_{\theta z}\) and \(\varepsilon_{r\theta}\) cannot propagate in fluid, which greatly reduces the energy dissipation to the internal medium. When the torsional mode is generated in pipe circumference uniformly, all the damage can be detected and, concurrently, the other flexural modes will be restrained to avoid overlapping between different modes.  
Two methods, pitch-catch and pulse-echo, which take advantage of reflected and transmitted waves, respectively, are usually used to inspect structural status. For the former, the transmitted signals contain multiple unknown damage information and arrive simultaneously, therefore, it is difficult to determine the specific damage location. In this paper, the pulse-echo method is employed consequently. Moreover, the reflected signals from damage can be considered as an indicator to quantify the damage.
## 3 Results
### Triaxial Shear Test
At lower contents of CLI, on increasing the content of CLI from 0 to 8%, the secant modulus of samples showed a notable increase from 2.68 to 23.85 MPa, 3.36 to 27.31 MPa, and 3.50 to 33.13 MPa under different confining pressures, respectively. With  
Figure 5: The deviatoric stress–strain curves and secant modulus of samples after 14 days of curing: (**a**) 100 kPa confining pressure; (**b**) 200 kPa confining pressure; (**c**) 300 kPa confining pressure; (**d**) Secant modulus. the increase in the content of CLI from 8% to 12%, a reduction in the secant modulus could be observed. Figures 4d and 5d show the variation in the secant modulus with the curing time. When the CLI content was 8%, the secant modulus of the modified soils increased from 5.80 MPa to 23.85 MPa, 7.13 MPa to 27.31 MPa, and 3.50 MPa to 33.13 MPa under different confining pressures as the curing time increased from 1 day to 14 days, respectively. It indicated that adequate curing was essential for CLI to improve the shear strength of the modified soil. Table 1 presents the peak deviatoric stress of samples for various curing periods and confining pressures. The results show that on increasing the confining pressure from 100 kPa to 300 kPa, the peak deviatoric stress showed a phenomenal increase for samples of 0-8% content of CLI. As for samples of 12% content of CLI, it can be observed that by increasing the confining pressure from 100 kPa to 300 kPa, the peak deviatoric stress increased first and then showed a reduction. In addition, with the increase in the content of CLI, the peak deviatoric stress increased to 8% content of CLI. This phenomenon became more remarkable as the curing period increased. But with the increase in content to 12%, it showed a reduction irrespective of the confining pressure and curing period. Moreover, the increase in the curing period played an active role in the enhancement of the peak deviatoric stress. It showed a more significant increase in increasing the curing period from 1 day to 7 days than from 7 days to 14 days. When the CLI content in the sample was 8%, the peak deviatoric stress of the samples after curing for 14 days was elevated compared to that of the samples cured for 1, 3, and 7 days by about 36.29%, 13.93%, and 2.11%, respectively, under the confining pressure of 100 kPa. This could be attributed as follows: after stabilizing the soil with CLI, it must take time to coat and bond soil particles to form a stable soil structure for the improvement of shearing resistance.
## 2 Theoretical Background  
### Gaussian Process (\(GP\))  
\(GP\) is a nonparametric model and consists of a set of random variables with the Gaussian distribution. For a \(GP\) model, it can be determined after selecting the mean function \(h(x)\) and covariance function \(c(x,x^{\prime})\). So a \(GP\) model can be expressed by using the following equation [29; 30].  
\[f(x)\sim GP(h(x),c(x,x^{\prime})) \tag{1}\]  
When used for regression, \(GP\) will encrypt the uncertainty before executing the training. Then, Bayes rule will be used to update the beliefs through the function and calculate the posterior distribution [31]. To the authors' best knowledge, the \(GP\) model has not been used in broken rock zone (BRZ) prediction, so a \(GP\) model associated with the rational quadratic covariance function was developed. For a high-performance \(GP\) development, it is necessary to select the optimal hyper-parameter combination, due to its huge impact on model performance. That combination can be obtained by using an optimization method or test-and-trial method, and the most widely used approach is to maximize the log marginal likelihood, and the conjugate gradient method is an effective method for these aims.  
It should be noted that only a brief introduction was described here, more theory of \(GP\) can be found in the study of [PERSON] [29].
# FREDC: A Few-Shot Relation Extraction Dataset for Chinese
## 3 Fredc
### Data Optimization  
After selecting the appropriate data sources, we analyzed their contents and identified several issues. First, we needed to standardize the dataset format to meet the requirements of the few-shot RE task. This process involved categorizing data by relation types and redistributing it into training, validation, and test subsets. To achieve this, it was necessary to re-screen the dataset to determine the relation type for each entry. Furthermore, to prevent the model from overfitting specific relations during training, it was essential to maintain a balanced distribution of relation types in the training data and ensure similar balance in the validation and test datasets for accurate evaluation.  
For the DuIE2.0 dataset, two main issues were identified. First, some sentences contained multiple relation types, and others were embedded with complex multi-relations. Since current few-shot RE research primarily focuses on sentences with a single relation type, we initially excluded sentences with multi-relations. We then filtered and reconstructed these sentences to ensure that each sentence in the dataset contained only one relation type. We also adjusted the amount of data for each relationship type in both the training and test sets to ensure a balanced distribution of data across the types. After this processing, we curated a set of 38 viable relation types, comprising over 100,000 instances of data.  
The CMefE-V2 dataset faced similar challenges with multi-relation sentences and also included some relation types with insufficient samples, limiting the construction of support sets. Due to the specialized nature of these relation types, extensive expansion was impractical, leading us to exclude them. In addressing relationship types with scalable data volumes, we employed ChatGPT to generate sentences for the designated types. This approach yielded nearly 700 additional pieces of data. After processing, we identified 29 usable relation types, totaling around 4000 entries.  
In the BBT-Fin Corpus, entity types were initially undefined, with only relation types specified. As a result, we first defined the entity types corresponding to the relations. Additionally, the dataset included passive relation types, such as \"acquisition\" and \"being acquired\". We opted to retain only the active behavior representations. Following further refinement, we finalized 22 usable relation types, amounting to approximately 3000 entries. To preserve the balance of the test set within this domain, we collected and manually annotated 200 pieces of data from 67 financial news items in Sina Finance.