esa-sceva/satcom-chunk-collection huggingface dataset:
# In Situ Neutron Radiography Investigations of Hydrogen Related Processes in Zirconium Alloys
## 3 Examples of In Situ Neutron Radiography Investigations
### Hydrogen Diffusion at Temperatures between 900 and 1400 \({}^{\circ}\)C
#### 3.2.2 Data Analysis  
During oxidation by steam, the total macroscopic neutron cross section of the samples changes by the absorption of oxygen, forming an oxide scale at the surface, and by the hydrogen uptake. In order to separate the effect of oxygen and hydrogen, the amount of oxygen bonded or absorbed by the sample was calculated by the known oxidation kinetics. The contribution of the oxygen to the total macroscopic neutron cross section \(\Sigma_{O}\) was determined in pretests:  
\[\Sigma_{O}\ =\ N_{O}\sigma_{O}\ =\ (0.98\ \pm\ 0.04)\ cm^{-1}\frac{\Delta m}{m} \tag{2}\]  
\(N_{O}\) and \(\sigma_{O}\) are the number density and microscopic neutron cross section of oxygen, \(\Delta m\) the mass gain after oxidation, and \(m\) the initial mass of the sample.  
Equation (2) was applied for the subtraction of the contribution of the oxygen to the increase in the total macroscopic neutron cross section of the sample measured in the in situ experiment. The dependence of \(\Delta m/m\) on the oxidation time and temperature was determined in former investigations of our own. Figure 9 gives an example of the separation of the effects of oxygen and hydrogen in the total macroscopic neutron cross-section development during oxidation by steam of Zircaloy-4 at 1200 \({}^{\circ}\)C.  
\begin{table}
\begin{tabular}{c c c} \hline  & **ICON** & **ANTARES** \\ \hline L/d & 600 & 400 \\ Thermal neutron flux, & & \\ n cm\({}^{-2}\) s\({}^{-1}\) & 3.9 \(\times\) 10\({}^{6}\) & 1.0 \(\times\) 10\({}^{8}\) \\ Pixel size, \(\mu\)m & 43 & 97 \\ Field of view & 44 mm \(\times\) 44 mm & 43.7 mm \(\times\) 43.7 mm \\ Illumination time per frame, s & 118 & 8.5 \\ Frame repetition frequency, Hz & 0.0083 & 0.1 \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of the in situ neutron radiography measurements.  
Figure 9: Contributions of oxygen and hydrogen to the increase of the measured total macroscopic neutron cross section of Zry-4 during steam oxidation at 1200 \({}^{\circ}\)C.
# The Cerebral Arterial Wall in the Development and Growth of Intracranial Aneurysms
## 6 Growth: Modification  
### Flow Dynamics and Aneurysm  
Aneuurysm dome stability, growth, or rupture is determined by this delicate altered vascular remodeling balance that takes place in the brain arterial wall through biological dynamic changes. Computational fluid dynamics (CFD) has been used by researchers to study the mechanical force on the vessel wall and understand the relationship between aneurysm formation, growth, and rupture [47; 48; 49]. Though CFD studies have revolutionized our understanding of different vascular pathologies, especially of atherosclerotic diseases and the hemodynamics of arterial bifurcations, controversies exist regarding aneurysm flow dynamics and subsequent growth [50]. Many in-vitro models were formed by considering only the laminar flow, but blood vessels are not just a mere collection of pipes. There is also the Circle of Willis with its branching points and angulations [51]. So, the complexity not only arises from the different geometric shapes of vessels and aneurysms, but there are also multiple interactions of the circulatory waveform with the phase shift, nonlinear dynamics, and multiple biophysical and pathologic processes [52; 53; 54; 55].  
Depending on blood flow, the following three types of stress have been identified on the vessel wall: the stress related to hydrostatic pressure, which works orthogonally; the shear stress, a frictional force acting tangentially to the vessel wall; the tensile stress, which acts circumferentially on the vessel wall [56]. Hemodynamic parameters such as wall shear stress (WSS), wall shear stress gradient (WSSG), oscillatory shear index (OSI), oscillatory index (OVI), transverse WSS, aneurysm formation indicator (AFI), and gradient oscillatory number (GON) have been devised to understand the complex relationship of aneurysm dynamics [57].
# Discrimination Accuracy of Sequential Versus Simultaneous Vibrotactile Stimulation on the Forearm
### Current Study  
We evaluated the recognition of Braille-like vibrotactile patterns among sighted observers with a 2 \(\times\) 3 array of voice coil actuators generating 100 Hz vibrotactile stimulation on the upper forearm. The patterns were categorized into two groups based on the number of actuators needed for their presentation, enabling us to assess the effects of pattern length\(-\)short (2-3 actuators) versus long (4-5 actuators)\(-\)on their recognition. Furthermore, we examined the potential for learning by observing performance improvement over time.  
Our primary objectives were as follows:  
1. **Presentation methods**: compare the efficacy of two vibrotactile pattern presentation methods on the forearm\(-\)simultaneous and sequential.
2. **Pattern length influence**: determine how varying the pattern length influences the accuracy of pattern recognition.
3. **Assessing temporal effects**: determine whether accuracy improves over time, and what the effect of stimulation order is during sequential presentation.
4. **Enhance user experience**: fine-tune these patterns to maximize user satisfaction and augment haptic feedback experiences.  
Our overarching goals align with our broader mission: to advance tactile communication. This objective carries immense potential, from improving accessibility to pioneering novel communication methods. In particular, our efforts can prove crucial for aiding Braille-proficient individuals with visual impairments. Our aim is to improve tactile information conveyance, creating a more inclusive environment for those with sensory challenges.  
Ultimately, we aspire to seamlessly integrate wearable technology into the daily routines of those with visual impairments. Through this, we envision expanding new opportunities for learning and interaction, thereby enriching their lives and fostering increasing societal involvement and participation.  
This study stands out through the investigation of simultaneous and sequential presentation methods, examining the influence of pattern length on recognition accuracy, and observing temporal effects in the realm of tactile communication and wearable technology.  
The primary objective is the seamless integration of innovative technology into the daily lives of visually impaired individuals, with a broader vision of fostering increased societal involvement in the future. The unique design of the vibrotactile forearm sleeve propels hands-free Braille communication, ensuring privacy and discretion. Beyond this, the study's implications extend to enhanced haptic communication, advancements in assistive technologies, and potential applications in virtual/augmented reality, which is particularly beneficial in low-visibility situations.
# Earth Mover's Distance-Based Tool for Rapid Screening of Cervical Cancer Using Cervigrams
## 2 Materials and Methods

Table 2: **Range measurement of different categorical verigrams:** this table depicts the EMD value-based computational tagging of cervigrams into different categories such as normal, abnormal, cervicitis, precancerous, and precancerous/cancerous lesions. The EMD values obtained from the cervigrams form the rationale for developing OM—the OncoMeter, a scale used for computational tagging of the cervigrams in the order of their severity, thereby facilitating the triaging of lesions. Calculation of the mean of EMD values was performed in order to obtain the standard normal cervigram for further EMD calculation and for a comparison with other subsets of  
Figure 4: Development of OM—The OncoMeter for triaging of subjects/patients with cervical cancer: this figure summarizes the impact of our study, where we have attempted to make a new scale to virtually rank and grade the cervigrams in the order of their normality or abnormality. This indeed forms the rationale for facilitating the rapid screening of cervical cancer in the rural milieus of the Indian sub-continent besides ranking the cervigram lesions in order of the severity of their disease progression. Such automated processing of chores will ultimately help clinicians to intervene in the patients in need of intervention on a priority basis. The use of this technology will also help in augmenting the hospital-based registry with community-based data, which will indeed provide a more realistic scenario of cervical cancer prevalence in resource-limited healthcare systems prevalent in the Indian sub-continent and elsewhere in the world. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Cases (Abnormal Cervigrams)—296** & **Controls (Normal Cervigrams)—102** \\ \hline True Positive & 251 & True Negative & 68 \\ False Negative & 45 & False Positive & 34 \\ Sensitivity (251/296) & 84.79\% & Specificity (68/102) & 66.66\% \\ Accuracy (319/398) & & 80.15\% & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: **Calculation of sensitivity, specificity, and accuracy of the proposed algorithm:** we used basic notions of sensitivity and specificity [15; 16] to quantify the performance of our proposed algorithm for early detection of cervical cancer as per standard definitions. This table essentially depicts the divergent data included in our study, which was tested by OM—The OncoMeter, and so far, the scale has produced promising results, with 84.79% sensitivity, 66.66% specificity, and 80.15% accuracy. abnormal cervigrams (Figure 4). Our calculation gave a mean value of 26.77. Cervigram number 52 was designated as the standard cervigram as all the normal cervigrams had the least distances from it with respect to EMD values (Figure 3). The image analytics algorithm-based automated processing of the cervigrams was programmed to obtain simultaneous generations of results so as to provide clinicians with a valuable adjunct clinical decision-making aide. We used the STARD checklist [18] when writing our report.
# A 3D Two-Phase Landslide Dynamical Model on GIS Platform
## 2 Methods
### Post-Processing  
In practical applications, our primary focus lies in determining the extent of landslide distribution and the thickness of deposits in the deposition area. In order to visualize the results of the three-dimensional model on the GIS platform, we drew inspiration from the SPH kernel function and transformed the computational results of the three-dimensional model into a two-dimensional format.  
Smoothed Particle Hydrodynamics (SPH) is an unstructured particle method based on the Lagrangian formulation, widely applied in engineering and scientific domains [43]. As the solution results of the three-dimensional model consist of a series of grid data, including the coordinates of points forming grid cells, the centroid coordinates of grid cells, and numerical values for various physical fields, we employ the following method to convert them into two dimensions.  
\[f(x,\ y)=\sum_{i=1}^{n}W(x_{i},\ y_{i})\times V(x_{i},\ y_{i})\times\ \alpha(x_{i},\ y_{i}) \tag{16}\]  
\[W(x_{i},\ y_{i})=1/\tau h^{2}\times e^{-\left(\frac{x_{i}^{2}+y_{i}^{2}}{h} \right)^{2}} \tag{17}\]  
\[h=\sqrt{2}\,\sigma \tag{18}\]  
where \(f(x,\ y)\) represents the elevation value at the two-dimensional grid \((x,\ y)\), \(V(x_{i},\ y_{i})\) represents the volume of the grid cell with centroid coordinates \((x_{i},\ y_{i})\), \(\alpha(x_{i},\ y_{i})\) denotes the volume fraction, indicating the proportion of the landslide body within the grid cell, \(W(x_{i},\ y_{i})\) represents the kernel function indicating the contribution of this grid cell to the two-dimensional grid \((x,\ y)\), and \(h=\sqrt{2}\,\sigma\) denotes the smoothing radius of the kernel function.  
Using the above method, the elevation values for the corresponding two-dimensional grid can be determined based on the known basic information of the three-dimensional grid cells.
# LoRaWAN Communication Protocols: A Comprehensive Survey under an Energy Efficiency Perspective
## 3 Communication Protocols
### Routing Protocols
In contrary to trees, in flooding topology, all nodes retransmit the packet received. This approach has been shown to be able to send packets in parallel without explicitly calculating routes. However, this topology has several constraints in terms of energy consumption and number of collisions if nodes transmit simultaneously.
## 3 Realization of Adaptive Square Root Cubature Kalman Filter Algorithm in Vector Receiver
### Realization of ASRCKF Algorithm in Vector Tracking Loop
#### 3.4.2 The Specific Implementation Steps of ASRCKF  
1. Time update (1) First, the SRCKF algorithm is used to obtain the state estimation value \(\hat{x}_{k-1|k-1}\) and the square root of the state estimation error covariance matrix \(S_{k-1|k-1}\) through the given model. Among them \(S_{k-1|k-1}=\sqrt{P_{k-1|k-1}}\). Let the process noise \(\mathbf{W}_{k}\) and the measurement noise \(\mathbf{U}_{k}\) be independent of each other, \(\mathbf{W}_{k}\sim N(0,\ Q_{k})\), \(\mathbf{U}_{k}\sim N(0,\ Q_{k})\). The initial value of process noise covariance \(Q_{0}=diag(1,1,1,1,1,1,1)\). The initial value of the measurement noise covariance \(R_{0}=diag(10,10\cdots\cdots 10)_{2n\times 2n}\). Use Equations (16) and (17) to calculate the cubature point and use the state equation to propagate it. The state transition matrix \(F\) is as follows: \[F=\left[\begin{array}{ccc}\mathbf{I}_{3}&\mathbf{T}\mathbf{I}_{3}&\frac{1}{2}T^{2}\mathbf{I} _{3}\\ \mathbf{0}_{3}&\mathbf{I}_{3}&\mathbf{T}\mathbf{I}_{3}\\ \mathbf{0}_{3}&\mathbf{0}_{3}&\mathbf{I}_{3}\end{array}\right]\] (32) \(m\) is the number of volume points. If \(n\) is used to represent the dimension of the state vector, then \(m=2n\) is used in the SRCKF algorithm. Additionally, \(\left[1\right]_{i}\) represents the \(i\)th column of matrix \(\left[1\right]\), and \(\left[1\right]\) represents the point set here: \[\left[1\right]=\left[\left(\begin{array}{c}1\\ 0\\ \vdots\\ 0\end{array}\right)\cdot\left(\begin{array}{c}0\\ 1\\ \vdots\\ 0\end{array}\right)\cdot\left(\begin{array}{c}0\\ \vdots\\ 1\end{array}\right)\cdot\left(\begin{array}{c}-1\\ 0\\ \vdots\\ 0\end{array}\right)\cdot\left(\begin{array}{c}0\\ -1\\ \vdots\\ 0\end{array}\right)\cdot\left(\begin{array}{c}0\\ -1\\ \vdots\\ 0\end{array}\right)\right]_{9\times 9}\] (33) (2) Calculate the predicted value of the state \(\hat{x}_{k|k-1}\) and \(S_{k|k-1}\), which is the square root of the covariance matrix of the prediction error \(\mathbf{W}_{k}\). Among them, \(S_{0,k-1}\) represents the square root factor of \(Q_{k-1}\), and \(Q_{k-1}\) represents the covariance matrix of the prediction error \(\mathbf{W}_{k-1}\). 2. Measurement update (1) The measured predicted value is obtained through Equations (24)-(26). \(S_{zz,k|k-1}\), the real-time square root of the innovation covariance matrix is obtained through Equation (30). (3) The real-time updated value of the state and square root factor of the state estimation error covariance matrix is obtained through Equations (22)-(26), and then enters the next cycle.
# Message Passing Detectors for UAV-Based Uplink Grant-Free NOMA Systems
## 3 Adaptive UAV-BS Detector Based on the HMP Rule  
In this section, the efficient GAMP algorithm is utilized to obtain the likelihood function for the signal \(b_{k,j},\forall k,j\). The novel GAMP-BG-MC algorithm is proposed, which can adaptively update the parameters and obtain the accurate marginal posterior PDF.
## 3 Solution Approach
### Obtaining Feasible Solutions  
Let \((y_{u},x_{u},I_{u})\) be the optimal solution of \(z_{u}\) for a given Lagrange multiplier \(u\); note that when the production periods are specified by fixing \(y_{il}\) at 0 or 1 for all \(i=1,\,\ldots,\,I\), \(t=1,\,\ldots,\,T\), the problem can be transformed into a transportation problem with \(\mid T\mid\) origins and \(\mid T\mid\times\mid I\mid\) destinations. Note also that some of the arcs of the transportation problem are forbidden due to either the prevention of backlogging or production not starting in that period.  
The transportation problem (_TP_) is formulated as follows.  
\[TP(y):\;\;\;z_{y}=min\sum_{i\in I}\sum_{I\in T}\sum_{\tau\in T}\delta_{ilt} \xi_{ilt\tau} \tag{13}\]  
s.f.  
\[\sum_{t\in T}\xi_{ilt\tau}\;\geq\;a_{i}d_{ilt},\;i\in I,t\in T \tag{14}\]  
\[\sum_{i\in I}\sum_{\tau\in T}\xi_{ilt\tau}\leq C_{t},\;t\in T \tag{15}\]  
\[\xi_{ilt}=0,\;\text{if}\;y_{il}=0\;\text{or}\;\;t>\tau;\,i\in I,t\in T,\tau\in T \tag{16}\]  
\[\xi_{ilt}\geq 0,\;i\in I,t\in T,t\in T \tag{17}\]  
\[\delta_{ilt}=\left\{\begin{array}{l}(p_{it}+\sum\limits_{q=t}^{\tau-1}h_{ ij})/a_{i},t\leq\tau\\ +\infty,\;\;otherwise\end{array}\right.,i\in I,t\in T,\tau\in T \tag{18}\]  
\(\xi_{ilt\tau}\): the amount of product \(i\) produced in period \(t\) to satisfy the demand in the future period \(\tau\).  
To solve the resulting transportation problem _TP_ (\(y\)), we develop a stepping-stone algorithm [41; 42], which is shown in Algorithm 1.  
```
Input: matrix \(X\), where \(x_{ij}\) is the number of units shipped from supply point \(i\) to demand point \(j\) Output: the optimal basic feasible solution (_bfs_) \(X\)
1: Determine an initial basic feasible solution (_bfs_) with the matrix minimum rule.
2: Calculate dual variables \(u_{i}\), \(i=1,\,\ldots,\,m,\,v_{j},j=1,\,\ldots,\,n\), for the current basis solution.
3: Find a non-basic variable with a negative reduced cost, min \(\lambda=u_{i}+v_{j}-c_{ij}<0\), which will enter the basis to improve the current basic solution; if such a variable \(x_{ij}\) is not found, then the optimal solution has been obtained; output the current _bfs_ and stop. Otherwise, go to step 4.
4: Find the closed loop that includes the newly found non-basic variable \(x_{ij}\).
5: Update the values of the elements along the found closed loop and obtain a new _bfs_.
6: Return to step 2.
```  
**Algorithm 1**Stepping-stone algorithm.  
Note that the solution to the transportation problem will give an upper bound \(z_{UB}\) for the CLSP if it is feasible. As an illustrative example from the literature [30], here, a 3-item \(\times\) 4-period CLSP with a specified \(y\) is considered. The problem and data are shown in Tables 1-3. The iteration process of the algorithm is shown in Tables 4-8.
# A Rapid Method for the Determination of SBS Content Based on the Principle of Orthogonal Testing
###### Abstract  
The performance of Styrene-butadiene-styrene (SBS) modified asphalt is closely related to the content of SBS modifier. In the production process of modified asphalt, a certain amount of additive such as sulfur and rubber oil may be added to reduce the segregation and promote the swelling of the polymer, but the effect of these additives on determining SBS content in asphalt is not yet clear. This paper presents the calibration curves of SBS content based on putting factor and creep slope and points out its defects according to the temperature scanning test and the bending beam rheometer test. Subsequently, using Fourier transform infrared spectroscopy (FTIR) for rapid determination of polymer content in SBS modified asphalt based on orthogonal test and then the effects of additives such as asphalt type, SBS content, rubber oil and sulfur on the accuracy of polymer content determination by FTIR were investigated. Moreover, in the orthogonal tests of adding sulfur and rubber oil, the multivariate analysis of variance (MANOVA) was firstly used to analyze the additives influence on the test accuracy of FTIR. Results indicated that the influencing degree of different additives is different. The influence of sulfur on the determination accuracy is greater than that of rubber oil. Therefore, the rapid determination method needs further improvement.  
SBS modified asphalt; high and low temperature performance; infrared spectroscopy; additives; orthogonal test +
Footnote †: journal: Applied Sci.
# Beyond the Horizon, Backhaul Connectivity for Offshore IoT Devices
## 2 Methodology
### Average Pathloss Using PE Simulation Tool  
Average path loss in ED at 5.8 GHz can be predicted using refractivity-based simulation models available in literature. Advanced Refractive Effects Prediction System (AREPS) software [19] based on Parabolic Equation (PE) model is used to predict average path loss for varying distances. We have simulated and calculated path loss variations for a 100 km distance over the Sea in the Malaysian region of the South China Sea with receiver antenna height of 4 m. The frequency is set at 5.8 GHz and the average value of day and night ED height variation in the Malaysian region is used to plot P.E based pathloss. The signal is trapped between the sea surface and ED layer to propagate beyond the horizon up to 100 km.
## 5 Numerical Examples
### Example 4  
We find the Moore-Penrose inverse of the following rank deficient matrix [45]:  
\[\mathbf{A}=\left[\begin{array}{cccc}-1&0&1&2\\ -1&1&0&-1\\ 0&-1&1&3\\ 0&1&-1&-3\\ 1&-1&0&1\\ 1&0&-1&-2\end{array}\right]. \tag{135}\]  
We apply the iterative method of the DOA as demonstrated in Section 5.2 to find the Moore-Penrose inverse, which has an exact solution:  
\[\mathbf{A}^{\dagger}=\frac{1}{102}\left[\begin{array}{cccccc}-15&-18&3&-3&18 &15\\ 8&13&-5&5&-13&-8\\ 7&5&2&-2&-5&-7\\ 6&-3&9&-9&3&-6\end{array}\right]. \tag{136}\]  
Under the same convergence criterion \(\varepsilon=10^{-9}\) as used by [PERSON] et al. [45], the iteration process of the DOA with \(m=1\) converges very fast in a total of 12 steps, as shown in Figure 3. In Table 3, we compare the DOA with other numerical methods specified by [PERSON] et al. [45] and [PERSON] [20] to asses the performance of the DOA measured by four numerical errors of the Penrose Equations (3)-(6) and the iteration number (IN).  
In the above, Alg. is the shorthand of algorithm, IP is the shorthand of initial point and IN is the shorthand of \"iteration number\". \(\mathbf{1}=(1,\ldots,1)^{\mathrm{T}}\). The first two algorithms were reported by [PERSON] et al. [45]. It can be seen that the DOA converges much faster and is more accurate than the other algorithms.  
Figure 3: A computation of the Moore–Penrose inverse by the DOA, showing the relative residuals in the computation of each column of the Moore–Penrose inverse matrix.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Alg.** & **IP** & \(\|\mathbf{AA^{+}A-A}\|^{2}\) & \(\|\mathbf{A^{+}AA^{+}-A^{+}}\|^{2}\) & \(\|(\mathbf{AA^{+}})^{\mathrm{T}}-\mathbf{AA^{+}}\|^{2}\) & \(\|(\mathbf{A^{+}A})^{\mathrm{T}}-\mathbf{AA^{+}A}\|^{2}\) & **IN** \\ \hline
[45] & \(10^{-5}\mathbf{A^{+}}\) & \(8.92\times 10^{-16}\) & \(3.36\times 10^{-16}\) & \(7.85\times 10^{-17}\) & \(1.97\times 10^{-15}\) & 50 \\ \hline
[45] & \(0.1\mathbf{A^{+}}\) & \(9.17\times 10^{-20}\) & \(6.99\times 10^{-16}\) & \(7.85\times 10^{-17}\) & \(2.86\times 10^{-15}\) & 60 \\ \hline
[20] & \(0.1\mathbf{A^{+}}\) & \(1.53\times 10^{-14}\) & \(9.21\times 10^{-17}\) & \(4.13\times 10^{-31}\) & \(1.17\times 10^{-30}\) & 165 \\ \hline DOA & \(\mathbf{x}_{0}=10^{-16}\mathbf{1}\) & \(5.21\times 10^{-27}\) & \(2.57\times 10^{-29}\) & \(3.82\times 10^{-27}\) & \(1.61\times 10^{-27}\) & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Computed results of a rank-deficient matrix in Example 4.  
In the computation of the pseudo-inverse, the last two singular values close to zero can be avoided to appear in the matrix \(\mathbf{C}\in\mathbb{R}^{m\times m}\) if we take \(m\) small enough. For example, we take \(m=1\) in Table 3, such that \(\mathbf{D}=\mathbf{C}^{-1}\) can be computed accurately without inducing a zero singular value to appear in the denominator to increase the rounding error.