esa-sceva/satcom-chunk-collection huggingface dataset:
# An Empirical Approach for Extreme Behavior Identification through Tweets Using Machine Learning
## 3 Materials and Methods
### Exploratory Data Analysis (EDA)  
EDA is known as a way of exploring dataset characteristics by computing basic statistical summary. The statistical summary about the dataset(s) is often used in combination with data visualization techniques in order to have better understanding of the dataset. EDA helps users to think beyond applying classical classification modelling algorithms. By applying EDA, we can have more detailed insights into the data with meaningful information that can also help to define/refine a hypothesis.  
In our analysis, we propose to apply Principal Component Analysis (PCA) as an EDA with an objective to transform a high-dimensional data space onto a low-dimensional space (usually 2-D or 3-D). Transformation of 2-D or 3-D through PCA will be useful to get a visualization plot that helps to improve our understanding of the dataset through visual observation in order to find natural groupings in the data. The problem with a tweet-based dataset and other natural text datasets is that it has several features (i.e., terms) and visualizing such high-dimensional features is not viable through classical information visualization tools, so we opted to use PCA to get transformed low-dimensional space in order to plot data on a scatter plot.  
Quite often PCA based dimensionality reduction methods are also used to get a transformed space of more than three-dimensions for datasets having dimensions from a few hundred to a few thousand. In such cases, dimensionality is reduced before applying classification model to reduce complexity of classification model by giving a small set of transformed features.
# A Security-Enhanced Query Result Verification Scheme for Outsourced Data in Location-Based Services
## 4 Proposed Scheme
### Data Verification Process
#### 4.4.2 Completeness Verification  
To check the completeness of the query result \(c(ds)_{\omega}\), the user first finds out the number \(Sum_{\omega}\) which indicates the available description files from the available result set \(ds_{\omega}\). Then, if \(Sum_{\omega}\) is not equal to the number of genuine description files \(\lambda_{\omega}\), the query result \(c(ds)_{\omega}\) can be directly judged as incomplete. Otherwise, the user finds out the corresponding available encrypted description files based on \(ds_{\omega}\). Further, for each available encrypted description file \(c\in c(ds)_{\omega}\), the user calculates \(\{h_{1}(prf_{k}(c)),\ldots,h_{l}(prf_{k}(c))\}\) with the set of \(l\) hash functions \(H\) and the corresponding counters \(vo_{\omega}[h_{1}(prf_{k}(c))],\ldots,vo_{\omega}[h_{l}(prf_{k}(c))]\) in \(VO_{\omega}\) are decreased by 1. Finally, the user can confirm whether the completeness of the query result \(c(ds)_{\omega}\) by judging whether \(R_{\omega}\) is equal to 0, where \(R_{\omega}=\frac{\sum_{l=0}^{n-1}vo_{\omega}[j]}{l}\). The process of completeness verification is shown in Algorithm 3.  
```
1:\(\lambda_{\omega}\), \(VO_{\omega}\), \(c(ds)_{\omega}\), \(ds_{\omega}\)
2: The completeness of \(c(ds)_{\omega}\)
3:\(Sum_{\omega}\leftarrow|ds_{\omega}|\)
4:if\(Sum_{\omega}\
eq\lambda_{\omega}\)then
5:\(c(ds)_{\omega}\) is incomplete;
6:else
7:for each available \(c\in c(ds)_{\omega}\)do
8: Calculate \(prf_{k}(c)\);
9: Calculate \(\{h_{1}(prf_{k}(c)),\ldots,h_{l}(prf_{k}(c))\}\) using \(H\);
10: The corresponding counters \(\{h_{1}(prf_{k}(c)),\ldots,h_{l}(prf_{k}(c))\}\) in \(VO_{\omega}\) are decreased by 1;
11:endfor
12: Calculate \(R_{\omega}=\frac{\sum_{l=0}^{n-1}vo_{\omega}[j]}{l}\)
13:if\(R_{\omega}\
eq 0\)then
14:\(c(ds)_{\omega}\) is incomplete;
15:endif
```  
**Algorithm 3** Completeness verification.
# A Simulation of Tire Hydroplaning Based on Laser Scanning of Road Surfaces
###### Abstract  
To investigate the influence of pavement texture on tire hydroplaning, this study utilized laser scanning to capture the surface characteristics of three asphalt mixtures--AC-13, SMA-13, and OGFC-13--across fifteen rutting plate specimens. Three-dimensional (3D) pavement models were reconstructed to incorporate realistic texture data. Finite element simulations, employing fluid-structure interaction and explicit dynamics in Abaqus, were conducted to model tire-water-pavement interactions. The results indicate that the anti-skid performance ranks as OGFC > SMA > AC. However, despite OGFC and SMA exhibiting comparable anti-skid metrics (e.g., pendulum friction value and mean texture depth), OGFC's superior texture uniformity results in significantly better hydroplaning resistance. Additionally, tire tread depth critically influences hydroplaning speed. A novel Anti-Slip Comprehensive Texture Index (ACTI) was proposed to evaluate pavement texture uniformity, providing a more comprehensive assessment of anti-skid performance. These findings underscore the importance of texture uniformity in enhancing pavement safety under wet conditions.  
laser scanning; numerical simulation; tire hydroplaning; pavement texture; anti-skid performance Article
# Artificial Neural Networks for Navigation Systems: A Review of Recent Research
## 4 Navigation Integration Enhanced by the ANN  
In complicated and hidden situations, inertial navigation is an essential component of vehicle navigation systems. There has been widespread use of the GNSS/INS combined system for land and air applications and DVL/INS for underwater applications. In general, the integration task makes considerable use of the EKF to obtain an accurate and consistent estimated position. In contrast, the lack of readings from the GNSS/INS EKF in the event of a GNSS signals failure, signal blockage, or jamming causes the navigation system's accuracy to rapidly decline. To increase the integrated navigation system's accuracy while GNSS failures, several fusion techniques have been investigated [39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64]. Adding additional sensors to compensate for the lost GPS information and improve navigation accuracy is possible but comes at the cost of increased system complexity. Machine learning algorithms have become famous for mimicking the INS's error propagation property and establishing a connection between updated vehicle dynamics and INS errors by using available GNSS signals to train the machine learning (ML) model. For the corrected INS mistakes caused by a lack of accurate measurements, such as GNSS failures, researchers have developed an error forecasting and compensating system based on artificial intelligence for GNSS/INS integration. The neural network has received considerable attention because of its useful nonlinear function estimation property and learning capacity. It has been frequently applied to integrated navigation error correction during GNSS failures. When GPS is unavailable, the neural network is used as an alternate integration mechanism to deliver superior navigational options.
###### Abstract  
This paper presents a novel Earth-System Stratified Grid (ISEA4H-ESSG) model, designed to address the challenges in multi-layer geoscience data management and analysis. In the realm of geosciences, which encompasses the solid earth, atmosphere, hydrosphere, and biosphere, as well as planetary and space sciences, the effective integration of diverse data sources is crucial. Traditional grids have limitations in three-dimensional spatial modeling, cross-layer data fusion, and dynamic multi-scale analysis. The ISEA4H-ESSG model overcomes these drawbacks by integrating the Icosahedral Snyder Equal-Area Aperture 4 Hexagon Discrete Global Grid System (ISEA4H DGGS) with a degenerative subdivision mechanism. It adheres to six core principles, including stratified spherical coverage, geographic consistency, multi-scale dynamic adaptability, global seamless partitioning, encoding uniqueness and efficiency, and multi-source data compatibility. Through the independent subdivision of spherical and radial layers, this model balances resolution differences and resolves polar-grid distortion and cross-layer data heterogeneity issues. The introduction of a four-dimensional spatiotemporal encoding framework enhances the storage and parallel computing capabilities of massive datasets. Case studies on ionosphere three-dimensional modeling and global atmospheric temperature field formatting demonstrate the high precision and adaptability of the ISEA4H-ESSG model. This research provides a unified spatial data infrastructure for geosciences, facilitating in-depth studies on natural hazards, climate change, and planetary evolution, and offering new perspectives for international partnerships and future Earth-related research.  
Keywords:discrete global grid system; multi-layer data fusion; geoscience modeling; spatial encoding; Earth-system science +
Footnote †: journal: Applied sciences
## 2 Materials and Methods
### Optimal Wavelength Extraction and Multispectral Model Development  
The PLSR models employing the full spectra of the hyperspectral data do not always yield good results due to the high dimensionality, multicollinearity, and redundancy among contiguous wavelengths, which also causes a lower processing speed and higher cost for hardware setup. Variable selection methods can be used to screen out useful wavelength variables, which can simplify the model development, improve model's performance and/or robustness, and benefit the development of online or portable instruments. In this study, two variable selection methods, i.e., uninformative variable elimination (UVE) and successive projection algorithm (SPA) were compared. UVE is one of the most prevalent variable selection methods that is widely used in analytical chemistry [25]; it is able to remove the variables that are not more informative than noise for modeling, and thus increase the model's predictive accuracy [26]. SPA is also a common method to select variables in multivariate modeling, and has been more favorable than the genetic algorithm [27]. SPA is an iterative forward selection method that adopts projection operations to choose variables of collinearity minimum.
## 5 Uniaxial and Biaxial Compression Tests  
Influenced by the test cases with the conventional damage model in reference [44,45], the failure processes of a rectangular rock specimen under uniaxial and biaxial compression were simulated for direct comparison. As shown in Figure 7a, the rectangular specimen had the same size and boundary conditions as the case in Section 4, except that the material was assumed to be mesoscale heterogeneous. Figure 7b shows the mesh grids and the initial elastic modulus of the rectangular specimen. The rectangle was divided into \(25\times 50=1250\) square grids with side length \(1\) mm.  
The mechanical parameters of each grid element were considered as random variables with a Weibull distribution, whose probability density function can be described as follows:  
\[f(u)=\frac{m}{u_{0}}\bigg{(}\frac{u}{u_{0}}\bigg{)}^{m-1}\exp\bigg{[}-\bigg{(} \frac{u}{u_{0}}\bigg{)}^{m}\bigg{]}, \tag{19}\]  
where \(u\) is a random variable; that is, the mechanical parameters of an individual grid element, such as elastic modulus, Poisson's ratio, and uniaxial tensile strength. \(u_{0}\) is the scale parameter related to the average value of variable \(u\). \(m\) is the shape parameter, the homogeneity index, which reflects the degree of material homogeneity.  
In this test case, the following mechanical parameters of the rock specimen were adopted: \(m=4\), \(E_{0}=60\) GPa, \(\sigma_{\text{t0}}=20\) MPa, \(n_{0}=107\), and \(\lambda_{\text{t}}=\lambda_{\text{s}}=0.1\). Moreover, four confining pressures were studied: \(pc=0\) MPa, \(10\) MPa, \(20\) MPa, and \(40\) MPa. Confining pressure was imposed on the specimen in the first loading step, and displacement increment was applied to the top boundary in the subsequent loading steps. A numerical sensitivity analysis of the loading step length on the uniaxial compression test indicated that the loading step length in a reasonable quasi-static range did not affect the macroscopic strength and the main crack propagation significantly [31]. Considering calculation accuracy and efficiency, a constant displacement increment of \(0.005\) mm/step was used here.
# Effects of Wind Barrier Porosity and Inclination on Wind Speed Reduction
## 2 Materials and Methods
### Statistical Analyses  
The software SPSS ver. 21.0 (IBM SPSS, Armonk, NY, USA) was used for statistical analyses. One-way analysis of variance (ANOVA), which is a statistical method to compare the means of three or more groups to determine if there are significant differences between them [40], was used to identify the range of the reduction in wind velocity for Cases A, B, and C. Following the ANOVA test, Duncan analysis, employed to determine which specific groups or means are significantly different from each other [40], was performed to classify identical groups of the average wind velocity for each inclination of Cases A, B, and C. The significance level was established at a 0.05 probability level for all statistical analyses.  
Figure 3: The 3 vertical measuring points in front of wind barriers and 3 vertical measuring points and 7 horizontal measuring points (i.e., 21 measuring points in total) behind wind barriers.
## 4 CSAMT Experiment in the Jinding Lead-Zinc Deposit
### CSAMT Data Quality Control
#### 4.2.2. CSAMT Data Acquisition Time Experiment  
During the field work, we also conducted experiments on acquisition time and obtained apparent resistivity sounding curves for different acquisition times. Through comparative analysis, we determined the optimal acquisition time parameters.  
Experimental conditions: With a single acquisition cycle of 40 min, three acquisition times of 40, 80, and 120 min were selected. Under the same survey line, survey point, equipment, and field source, three apparent resistivity sounding curves were obtained. The experimental results for the different acquisition times are shown in Figure 9.  
As can be observed in Figure 10, the apparent resistivity sounding curve with an acquisition time of 40 min exhibits poor continuity of resistivity values, especially below 30 Hz. The apparent resistivity curve appears jagged, and the characteristics of the transition zone and the near zone are not pronounced, indicating poor data quality. Compared to the apparent resistivity curve for the 40 min acquisition time, the apparent resistivity sounding curve for 80 min is smoother and more continuous, with smaller jumps. Below 30 Hz, it starts to enter the near zone, and the curve rises at an angle exceeding 45\({}^{\circ}\). Meanwhile, there are jump points, and the transition zone characteristics are not pronounced, indicating intermediate data quality. The apparent resistivity sounding curve for the 120 min acquisition time is smooth and exhibits good continuity. The characteristics of the far zone, the transition zone, and the near zone are distinct. A minimum value appears at 10 Hz, which is a characteristic of the transition zone. Below 10 Hz, the sounding curve rises at an almost 45-degree angle, which is a typical characteristic of a CSAMT sounding curve, indicating high data quality.  
Therefore, in the Jinding lead-zinc mining area, by extending the data acquisition time and increasing the stacking number, the characteristics of the apparent resistivity sounding curve at the measurement points can be significantly improved, thereby enhancing the quality of CSAMT single-point data.
## 3 Magnetic Data Analysis
### Quantitative Interpretation of Hebron Magnetic Anomaly  
The Hebron magnetic anomaly is the most sizable magnetic anomaly observed in Israel's territory. It occurs in the vicinity of the Negev, Heletz, and Judea-Samaria terranes. Some investigators have related this anomaly to magnetized bodies at comparatively small depths (5-7 km). However, quantitative analysis of airborne data (1000 m over the Earth's surface) shows that the anomalous magnetic body upper edge occurs at a depth of 26-28 km [47] (Figure 8). A rough estimation of the magnetized lower edge occurrence (the non-magnetized lower part may continue down for tens of km) gave a depth of about 40 km. This body's magnetization and the magnetization vector's inclination are estimated as 3000 mA/m and 80\({}^{\circ}\), respectively. [PERSON] and Curie's discontinuity maps [29]  
Figure 7: Quantitative examination of the Rosh-Ha-Ain magnetic anomaly (see its location in Figure 3). (1) Magnetization of the anomalous body and surrounding medium, (2) position of the magnetization vector.  
display that in the area, the corresponding surfaces have values of 29-30 and 40-40.5 km, respectively. Hence, the anomalous body's upper edge occurs 3 km above the calculated Moho boundary, and the lower edge of this body coincides with the maximum possible depth of magnetic target occurrence. Tectonically, the area of the Hebron anomaly relates to the collision zone between the Judea-Samaria and Negev terranes. The sequence formation maps were developed based on the data from several deep boreholes (\(\sim\)5-6 km) in this area. These maps indicate that the Judea-Samaria terrane was subducted under the Negev terrane. Considering that the collision zones are characterized by recognizable geodynamic mantle-crust instability, we propose that a mantle diapir is the source of the Hebron magnetic anomaly.  
Figure 8: Quantitative analysis of the Hebron magnetic anomaly (see its location in Figure 3). (1) Excess magnetization of the anomalous body, (2) position of the magnetization vector. Since the constructed Curie discontinuity map [29] indicates a depth of 40–40.5 km in this area, below this depth, the magnetization \(\Delta J=0\). In some intermediate zone (at 40.5–39.5 km), \(\Delta J\)–100 mA/m, and over this zone (39.5–28 km), this anomalous body has the determined magnetization: \(\Delta J=2500\) mA/m.
## 3 Robotic Rehabilitation with FES for SCI  
The following factors have been listed as important in planning a rehabilitation program: (1) dose: frequency/long-term repetition of nerve input, (2) quality: quality of nerve input, and (3) paired associative stimulation: synchronization/consistency of nerve input [55]. In gait rehabilitation, the \"quantity\" of repeating correct gait movements and the \"quality\" of appropriate neural input according to the gait cycle are considered to be important. The midbrain gait induction field of the brain stem and the central pattern generator (CPG) composed of spinal cord interneurons play a role in the expression and drive of gait movement [56]. It is often difficult to repeat and continue walking training of sufficient quantity and quality via conventional rehabilitation treatment by a physiotherapapist alone. With this in mind, rehabilitation robots can provide valuable assistance by providing an amount of training as well as motor learning that cannot be obtained by conventional training.  
The pioneer rehabilitation robot for SCI is the Lokomat(r), a treadmill walking robot based on the CPG theory of [PERSON] et al. [57]. The Lokomat(r) is an exoskeleton-based traveling robot that assists stepping by robot control, and achieves walking movement patterns of the lower limbs by motors installed in the hip and knee joints [58]. The effectiveness of walking training by robot rehabilitation for people with SCI has also been reported for Wearable Power-Assist Locomotor (WPAL) and hybrid assistive limb (HAL) [59; 60].  
It is difficult for paralyzed limbs to perform automatic exercises with muscle contraction even with walking training by robot rehabilitation. However, training can be performed with automatic muscle contraction from the early stage of onset when a robotic system is used together with FES. Muscle fatigue is a known problem with FES alone; however, robot assist can compensate for muscle fatigue, and interactions within the hybrid system prevent disuse, suppress spasticity, and effectively re-educate neuromuscular muscles. We have developed a walking training rehabilitation robot with FES for hemiplegics [61], and the development of equipment for use with paraplegics is underway (Figure 4).  
Figure 4: A walking training rehabilitation robot with functional electrical stimulation (FES) for paraplegia. The exoskeleton is made based on the long leg orthotic device, and the rehabilitation unloading lift (black thick arrow), FES and robot orthotic device (white thick arrow) are attached, and walking exercise is performed on the treadmill.
# An Efficient and Secure Fog Based Routing Mechanism in IoT Network
## 3 Methodology
### Direct Trust  
The method of gathering data for trust computation is deemed one of the most crucial elements of trust management schemes. The direct trust value of any node is computed by considering the numerous trust metrics of that particular node in various events of the network. The trust metrics, or the QoS attributes that can be considered are listed in as 0 to 1 [1]. The provided trust metric information for various events is crucial and can help the system's trust management system for the node to make the right decisions. The direct trust is determined by the sending device itself while taking into account trust parameters. As a result, Equation (1) is used to determine the direct trust value of the nearby nodes so that every node can ascertain the direct trust value of each of its neighbors by using this equation.  
\[T.Direct_{xy}^{n}=\theta\times(\alpha_{1}V_{t}+\alpha_{2}{B_{t}})^{n-1}+(1- \theta)\times(V_{t}+B_{t})^{n} \tag{1}\]  
The earlier portion of this equation illustrates the historical trust value, and the later part represents the current trust value. Moreover, the variables \(\theta\) and \((1-\theta)\) are weighting parameters and have been employed to adjust the current and past trust values. The \(n\) is very important here which represents the number of nodes participating in an IoT network as the initial and current state of the network, whereas \(x\) and \(y\) show the coordinates and location of the nodes. In addition to this, the value of \(\theta\) should be greater than 0 and less than 1 as per the specific application of the IoT network, whereas \(\alpha_{1}\) and \(\alpha_{2}\) are the volatilization factors and have been added to reduce the previous trust values of malicious nodes. Additionally, the symbols \(V_{t}\) and \(B_{t}\) represent the ratios of the total number of received and sent messages, respectively, out of all messages and are calculated using Equation (2).  
\[V_{t}=\frac{Get\_Message_{y}}{Total\ Message_{y}} \tag{2}\]  
Equation (2) is used to compute the ratio of received data packets out of all packets of sensor \(y\), whereas Equation (3) is used to calculate the ratio of sent data packets out of all packets.  
\[B_{t}=\frac{Sent\_Message_{y}}{Total\ Message_{y}} \tag{3}\]