esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection of Pediatric Femur Configuration on X-ray Images
## 2 Materials and Methods
### Training Stage: Gradient-Based Estimator  
As stated previously, border points of bone shafts can be used to calculate the LA keypoints. Assume a set of points on each border of the bone shaft  
\[\mathbf{p}_{p}\triangleq\left\{p_{p_{k}}\right\}_{k=1}^{\lambda},\quad\mathbf{p}_{a} \triangleq\left\{p_{a_{k}}\right\}_{k=1}^{\lambda}, \tag{8}\]  
where \(\lambda\) is a predefined number of auxiliary keypoints to be assigned on each side of the border, for the posterior and anterior side, respectively. The first point in each set represents an intersection between the bone shaft and the fluoroscopic lens, as presented in Figure 7b.  
The proposed algorithm of LA keypoints estimation is described as Algorithm 1. The steps will be discussed briefly. First, the input image is converted to binary by the adaptive threshold technique [30] with a randomly chosen window size \(s\). Second, the binary image is opened to cancel all small objects. The points of remaining objects that intersect with the fluoroscopic lens are detected (round markers in Figure 3) and are used as initial conditions to the Moore-Neighbor tracing algorithm [31]. An eight pixel neighborhood is chosen and border points \(\mathbf{p}_{p}\) and \(\mathbf{p}_{a}\) are extracted. Each border is approximated using simple linear regression [32]. Both linear approximations are verified in terms of the coefficient of determination, denoted as \(R^{2}\). If both bone borders are estimated correctly, i.e., the linear regression of points can be correctly approximated by a straight line, \(R^{2}\) is high and the algorithm passes to the next step. If the condition is not satisfied, a different adaptive threshold window size \(s\) is chosen, and all of the steps are repeated for a new binary image.  
```
Result:\(\mathbf{k}_{4}\), \(\mathbf{k}_{5}\) Input: X-ray image;  Set \(\mathbf{s}=11+2\cdot j\), \(j\in\left\{0,\dots,15\right\}\);  Set \(\lambda=50\), \(flag=0\); whileflag = 0 do  Randomly select window size \(s\in\mathbf{s}\);  Binarize image with \(s\)[30];  Cut small objects (binary image opening);  Find initial points \(p_{p_{1}}\) and \(p_{a_{1}}\) on binary image;  Trace \(\mathbf{p}_{p}\) and \(\mathbf{p}_{a}\) using [31];  Calculate linear regression of \(\mathbf{p}_{p}\) and \(\mathbf{p}_{a}\) with [32]; if\(R^{2}>95\%\)then  flag = 1;  end if  end while  Calculate \(\mathbf{p}_{LA}=\left(\mathbf{p}_{a}+\mathbf{p}_{p}\right)/2\);  Set \(\mathbf{k}_{4}\) and \(\mathbf{k}_{5}\) on linear regression of \(\mathbf{p}_{LA}\);
```  
**Algorithm 1**LA keypoint estimation.  
As soon as bone borders are estimated correctly, the mid-points between the borders are obtained. Their linear regression comprises the LA line. Keypoints \(\mathbf{k}_{4}\) and \(\mathbf{k}_{5}\) are assigned as random points on a linear approximation of LA.
# Gender Classification Using Sentiment Analysis and Deep Learning in a Health Web Forum
## 3 Experiment
### Classification Algorithms
#### 3.3.3 Random Forest  
Random forest is an algorithm that generates multiple decision trees by randomly sampled data for learning and assembles the results of the decision trees by majority voting. It is a type of ensemble leaning method that implements classification, regression, and clustering based on group learning. Random forest trains the model using the subset of features and the subset of data in a repetitive way. This operation reduces the risk of overfitting. This algorithm has been widely used in various applications in recent years and has been proved to outperform other algorithms.
## 3 Test Results and Discussion
### Microstructural Analyses  
#### 3.6.1 XRD & FTIR Analysis  
X-ray diffraction (XRD) analysis serves as a crucial method for detailing alterations in material properties, interactions among interlayers, and the impact of added particles, providing an extensive set of data for interpretation regarding soil minerals. Figure 11 displays the XRD outcomes for samples that are both untreated and those treated with a MgCl\({}_{2}\) 14% solution. Upon comparison, there is no noticeable difference in the diffraction patterns between the untreated and treated samples. Specifically, no new peaks were observed in the diffraction pattern of the treated soil, and the peak positions, indicated by the 20 values, remained unchanged for both sets of samples. This constancy suggests that the interlayer spacing remained unaltered, no new chemical bonds were formed, and the MgCl\({}_{2}\) solution did not replace the interlayer cations within the clay structure. The primary mechanism for the observed phenomena is likely attributed to electrostatic exchanges between the clay particles and the charges of the MgCl\({}_{2}\) solution, which contributes to an enhancement in the soil strength.  
Fourier-transform infrared (FTIR) spectroscopy was employed to detect functional groups within untreated and treated soil minerals. Samples' FTIR spectrum shows different peaks that are indicative of the many functional groups and bonding interactions that are typical of clay minerals (Figure 12). The spectra included bands at 688.16 cm\({}^{-1}\) and  
Figure 11: XRD profiles or untreated and 14% MgCl\({}_{2}\)-treated soil.  
776.98 cm\({}^{-1}\), which are likely indicative of metal-oxygen bonds, suggesting the presence of inorganic components or impurities within the clay matrix. Additionally, peaks at 910.10 cm\({}^{-1}\) and 997.46 cm\({}^{-1}\) were observed, indicating Si-O stretching vibrations and supporting the presence of silicate minerals such as quartz and potentially muscovite in the clay. The peak at 1634.69 cm\({}^{-1}\) is due to bending vibrations of water molecules (H-O-H bend), which indicate moisture content in the clay. Peaks at 3620.48 cm\({}^{-1}\) and 3694.75 cm\({}^{-1}\) were recognized for the presence of O-H stretching vibrations from hydroxyl groups. The first peak is typical of phyllosilicates, while the second peak is specific to kaolinite, implying a rich composition that includes kaolinite and possibly other phyllosilicate minerals. Overall, comparisons of the FTIR spectra of untreated and 14% MgCl\({}_{2}\) solution-treated soils showed no significant differences, indicating that MgCl\({}_{2}\) does not significantly affect the soil's functional groups. The only difference observed was the emergence of a peak at 3371.95 cm\({}^{-1}\), which suggests enhanced O-H stretching vibrations, possibly due to new or altered hydroxyl groups or changes in hydrogen bonding influenced by interactions with Mg\({}^{2+}\) ions. This comparison also suggests that the treatment mechanism is based on physical (electrostatic) interactions.
# Enhancing Reliability of Tactical MANETs by Improving Routing Decisions
## 6 Discussion  
In tactical MANETs, the responsiveness, reliability and robustness are more important than scalability. These networks are often deployed in terrains where obstructions weaken transmitted signals and shorten transmission ranges. Limited link budget, constrained bandwidth, unwanted interference (jamming) and multipath propagation conditions are main contributors constraining communication capabilities. In addition, connectivity in tactical MANETs is highly dynamic and multi-hop routes may not be stable enough for on-time delivery of mission critical messages.  
The primary goal of this paper was to propose and evaluate a suitable routing protocol which can meet requirements of tactical MANETs. Effective design of such protocol needs to assume militarily-realistic scenario with mobility in order to capture dynamic network behavior. This requires significant background knowledge on tactical mission planning and deployment of tactical MANETs including realistic modeling of the radio propagation environment, mobility patterns, radio equipment type, and antennas which are used to maintain connectivity. In the proposed reactive routing protocol, termed LQA-AODV, RSS and SINR measurements were combined to make routing decisions. RSS is used for initial fast decisions whereas SINR can provide more refined decisions. The proposed protocol was simulated in ns-2 to evaluate its performance against other AODV protocols, namely AODV, H-AODV and AOMDV. The performance comparisons were done assuming 3 metrics: Throughput, PDR and end-to-end delay. For conditions encountered in typical tactical missions, the proposed protocol was found to clearly outperform the other routing protocols. We can conjecture that combining multiple network measurements to improve the protocol performance in wireless networks is an important and largely unexplored problem to consider.  
**Author Contributions:** Formal analysis, P.L.; Writing--review & editing, S.M.A.-S.  
**Funding:** This research received no external funding.  
**Acknowledgments:** In this section you can acknowledge any support given which is not covered by the author contribution or funding sections. This may include administrative and technical support, or donations in kind (e.g., materials used for experiments).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 8: (**a**) End-to-end delay vs. node speed. (**b**) End-to-end delay vs. network diameters. (**c**) End-to-end delay vs. node density.
## 5 Case Study: Experimental Results
### User Behavior Analysis
#### 5.3.2 Herd Behavior and Event Detection  
We can also detect herd behavior of users affected by high-impact social events. Figure 6(a) shows the topic flow of the PGR21 dataset with \(K=8\). We see peaks in Topic 2 during the second half of the timeline. At that time, a series of events occurred in South Korea concerning the impeachment of the president, and the election of a new president. Moreover, around this time, community rules forced articles on sport and entertainment to be uploaded to a separate bulletin board, which gave the remaining users fewer choices of topics. As a result, almost all users in the community show a shared interest in this topic, resulting in the single-topic peaks at that time. Figure 6(b) shows another visualization of this topic flow using the dominant topic of articles, instead of topic distribution in individual assignments. For example, if an article has a topic distribution \(\langle 0.1,0.1,0.2,0.6\rangle\) with \(K=4\), we simply count 1 for Topic 4 instead of summing the distribution vector itself. In this visualization, we can analyze the topic flow more focused on articles, while the original visualization places more emphasis on the user perspective. The peaks are observed much more significantly, which means the articles with this topic dominated the community. The time interval between two peaks of Topic 2 is the period between the impeachment judgment and the presidential election.
## 3 Operational Requirements
### Navigation and Mobility
#### 3.2.2 Route Planning and Navigation
* **GPS and LiDAR integration:** While GPS provides the robot's global location in the field, LiDAR provides detailed mapping of the immediate environment. By combining this data, the robot can plan and execute harvesting routes more efficiently and safely. GPS and LiDAR data are integrated using sensor fusion algorithms that combine the advantages of both systems to improve navigation accuracy and obstacle detection. As the robot moves, GPS and LiDAR data is continuously updated, allowing the robot to adapt to changes in the environment and maintain an optimal trajectory. Figure 4 shows a robot in the process of harvesting, to achieve this it must have a very precise positioning system due to the difficult conditions of its environment.
###### Abstract  
This study investigates numerically gaseous CO\({}_{2}\) leakage characteristics inside the containers of a transport ship and examines thermal effects on the structural damage that might happen in the containers. First, with consideration of the phase change, the ejected mass flow rate was estimated using the commercial code of DNV PHAST. Based on this estimated mass flow rate, we introduced an effective area model for accounting for the fast evaporation of liquefied CO\({}_{2}\) occurring in the vicinity of a crack hole. Using this leakage modeling, along with a concept of the effective area, the computational fluid dynamics (CFD) simulations for analyzing transient three-dimensional characteristics of gas propagation in a confined space with nine containers, as well as the thermal effect on the walls on which the leaking gas impinges, were conducted. The commercial code, ANSYS FLUENT V. 17.0, was used for all CFD simulations. It was found that there are substantial changes in the pressure and temperature of the gas mixture for different crack sizes. The CO\({}_{2}\) concentration at human nasal height, a measure of clear height for safety, was also estimated to be higher than the safety threshold of 10% within 200 s. Moreover, very cold gas created by the evaporation of liquefied CO\({}_{2}\) can cool the cargo walls rapidly, which might cause thermal damage.  
computational fluid dynamics (CFD); crack; gas leakage; phase change; propagation; transport ship +
Footnote †: journal: Applied Sciences
# Eigendegradation Algorithm Applied to Visco-Plastic Weak Layers
## 3 Time and Spatial Discretization
### Time Discretization  
In this work, an implicit scheme has been proposed, since several applications cover a wide range of loading rates from slow scenarios to quick phenomena. For the first ones, an explicit scheme would provide long computation time. Thus, the Newmark Implicit Scheme has been employed, with the parameters \(\gamma=0.6\) and \(\beta=0.325\) that are known to be suitable for dynamic problems [58]. To construct this scheme, Equation (24) is reformulated as a system of equations, which reads as  
\[\mathbf{R}_{k+1}+\mathbf{M}\,\ddot{\mathbf{u}}_{k+1}=\mathbf{P}_{k+1}, \tag{30}\]  
where \(\mathbf{R}\) and \(\mathbf{M}\) denote the internal forces vector and mass matrix, respectively. \(\mathbf{P}\) holds for the external forces vector, which is composed by both gravity acceleration and external nodal forces as we have seen previously. Equation (30) can be re-written with the Newmark scheme as:  
\[\mathbf{G}_{k+1} = \mathbf{M}[\alpha_{1}\Delta\mathbf{u}_{k+1}-\alpha_{2}\dot{\mathbf{u}}_{k}- \alpha_{3}\ddot{\mathbf{u}}_{k}]+\mathbf{R}_{k+1}-\mathbf{P}_{k+1}=\mathbf{0}, \tag{31}\]  
where the \(\alpha\)-parameters are calculated according to Wriggers [59] and are listed in Table 1. These coefficients can be easily extended to any other time integration schemes.  
When the above non-linear equations are solved through a Newton-Raphson method, the resulting iterative scheme, taking into account the matrices that are involved in our problem, can be written as:  
\[\begin{array}{rcl}\left[\alpha_{1}\mathbf{M}+\mathbf{K}_{k+1}^{i}\right]\Delta\mathbf{u }_{k+1}^{i+1}=[\mathbf{K}_{*}]_{k+1}^{i}\Delta\mathbf{u}_{k+1}^{i+1}&=&-\mathbf{G}(\mathbf{u}_ {k+1}^{i}),\\ \text{where}\ \ \mathbf{u}_{k+1}^{i+1}&=&\mathbf{u}_{k+1}^{i}+\Delta\mathbf{u}_{k+1}^{i+1}. \end{array} \tag{32}\]  
where \(\mathbf{K}\) is the derivative of the internal forces of each iteration \(i\), which is also known as the _tangential stiffness matrix_:  
\[\mathbf{K}(\mathbf{u}_{k+1}^{i})=\mathbf{K}_{k+1}^{i}=\left.\frac{\partial\mathbf{R}}{\partial \mathbf{u}}\right|_{\mathbf{u}_{k+1}^{i}}. \tag{33}\]  
The iteration procedure finishes when the norm of the residuum \(\mathbf{G}_{k+1}^{i}\) is below a given tolerance.
## 2 Theory and Method
### Theory of the PSO-GD Algorithm
#### 2.2.1 \(\alpha\)-Trimmed Filter Constraint  
The missing a priori information in an inverse problem and the problem of over stochasticity associated with PSO result in abrupt changes in the information for the model parameters in the inversion result in the 3D space. To address this problem, a regularization constraint term is added to the objective function in a conventional deterministic inversion algorithm, with the goal of stabilizing the inversion process and producing a smooth and reasonable result. The equation of the objective function is as follows:  
\[F(m)=\frac{\phi_{0}-\phi_{c}}{\sigma_{\phi}}+\lambda\big{\|}\log_{10}(\partial m )\big{\|} \tag{25}\]  
where \(\phi_{0}\) is the observed signal, \(\phi_{c}\) is the calculated response, and \(\lambda\) is referred to as the Lagrangian multiplier or smoothing parameter. The right-hand side of Equation (25) consists of two terms: the first term evaluates the difference between the observed data and the response calculated by the forward simulation model, and the second term realizes the regularization constraint through the minimization of the roughness of the model.  
In contrast with conventional model-driven algorithms, a mean filter is generally used to smooth the model during the inversion process of the PSO algorithm equipped with a regularization term [26]. In this study, the \(\alpha\)-trimmed filter is used as a regularization constraint strategy for the PSO-GD algorithm. Prior to the start of each iteration, the individual optimum solutions \(P_{i}\) in the particle swarm are successively filtered. First, a fixed window width n is selected based on the scale of the constraint. Then, 3D data centered at the location of the constraint are successively read with a sliding window. The data selected along the \(x\)-, \(y\)-, and \(z\)-axes are sorted in ascending order. Subsequently, a trimming parameter \(\alpha\) is selected to trim off the same number of points from the front- and back-end segments of the sequence, with the goal of eliminating the interference from extreme values. Finally, the mean of the remaining points is used as a substitute for the value at the center of the window [54]. The whole process is shown in Figure 1. The 3D filtering equation is as follows:  
\[m(n_{x},n_{y},n_{z})=\frac{1}{3(n-2\alpha n)}\left(\sum_{j=\alpha n+1+n_{x}}^{ n-\alpha n+n_{x}}\big{(}m\big{(}j,n_{y},n_{z}\big{)}\big{)}+\sum_{j=\alpha n+1+n_{y}}^{ n-\alpha n+n_{y}}\big{(}m(n_{x},j,n_{z})\big{)}+\sum_{j=\alpha n+1+n_{z}}^{n- \alpha n+n_{z}}\big{(}m\big{(}n_{x},n_{y},j\big{)}\big{)}\right) \tag{26}\]  
While smoothing the model, the trim filter can also remove the impact of extreme values, thereby sharpening the boundary information and allowing the anomalies to converge.  
To sum up, Figure 2 shows the flowchart of the PSO-GD algorithm developed in this study.
# Hybrid Path Planning for Efficient Data Collection in UAV-Aided WSNs for Emergency Applications
## 4 Hybrid Algorithm for UAV Path Planning
### Constraints for Path Planning  
#### 4.2.1 Collision-Free Path  
A collision can occur in either static or dynamic obstacles. To achieve a feasible path plan, the characteristics of all obstacles must be identified and avoided. Hence, if the function \(g(X_{i},\ Y_{i})\) returns the land's altitude at a certain point, then the equation that expresses the total number of waypoints falling into an obstacle is as follows.  
\[W_{obs}=\sum_{i}Z_{i}A_{i}=\left\{\begin{array}{c}1\ \text{if}\ Z_{i}\ \
eq g(X_{i},\ Y_{i})\\ 0\ \text{otherwise}.\end{array}\right., \tag{13}\]
# Journey Planning Algorithms for Massive Delay-Prone Transit Networks +
## 3 Basic Public Transit Labeling  
The state-of-the-art method (in terms of query time) to solve the journey planning problem is commonly referred to as Public Transit Labeling (ptl) [13]. The technique comes in two flavors: a basic version to answer to earliest arrival and profile queries only, and an extended, more general version to incorporate generic criteria of optimization, for example, to seek for earliest arrival journeys that also minimize the number of transfers. The basic version essentially consists of three main ingredients:  
1. a reduced time-expanded graph, a well-known data structure for storing transit networks (see e.g., Reference [9]);
2. a reachability labeling, a compact labeling-based representation of the transitive closure of the said graph, computed via a (time-consuming) preprocessing step;
3. an efficient query algorithm exploiting both the graph and the labeling to answer quickly to queries of interest at runtime.  
In what follows we describe such ingredients in detail.
## 4 Applicability Analysis of Theoretical Calculation Method of Loose Zone  
Analysis was performed on the single-track tunnel of the Qingdao Metro Line 1 project. Qingdao has a typical soil-rock binary composite stratum structure. In general, the surrounding rock is covered with quaternary soil layers of different thicknesses on a granite rock foundation with strong, medium, and slight weathering degrees. The station and interval tunnels pass through various weathered rock and soil layers, which are very uneven in the longitudinal direction, and the buried depth is about 17-26 m.  
The acoustic method was adopted for the loose zone range field test. The process of the acoustic detection of the surrounding rock loose zone included the post-processing of the acoustic emissions, propagation, and reception display. The combination of a single hole, one generator, and two receivers was adopted for the test. The corresponding instruments were composed of a transmitting transducer, two receiving transducers, an RSM-RCT(B) acoustic logging tool, a liquid crystal display, and peripheral equipment. Inserting the test sensor assembly into the pre-drilled hole, first inject air into the air bag to block the hole, and then turn on the water pump. Then, start the transmitter-receiver T, and the receivers R1 and R2, respectively, receive the wave signal after different delays. The propagation speed of the sound wave in the rock mass at this position can be obtained through the computer program connected to the sensor assembly. Then, by changing the location of the test points, the rock mass propagation velocities at different depths can be measured, and these velocity points can be compared and analyzed to obtain the relevant data information of the whole tunnel loose circle. The test instrument and working principle are shown in Figure 5.  
The field test diagram is shown in Figure 6.  
Figure 5: Loose zone testing principle and instrument: (**a**) Loose zone test principle, (**b**) Loose zone test instrument. Figure 6: Loose zone range field test. Three typical working conditions were selected for the survey: the section of entrance B of Anshun station, the section of Anshun Road Station to Qingdao north station, and the section of Shuiqinggou station to Kaifeng Road station. The three tunnel sections had the same section form, which is shown in Figure 7.  
The measured results of the field acoustic method under three working conditions are shown in Figures 8-10. The results of the surrounding rock conditions and loose zone thickness obtained from the measured results and geological exploration data are shown in Table 2.  
Figure 8: Monitoring waveform diagram of working condition 1: (**a**) Vault waveform; (**b**) spandrell waveform. Figure 7: Structural dimensions of the tunnel section (mm). According to the field-measured waveform data, wave velocity analysis was carried out by the acoustic method. The wave velocity of the complete rock mass was generally high, while the wave velocity decreased relatively in the loose area of stress decline and fracture expansion. Therefore, there were significant wave velocity changes between the