esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancements and Challenges in CoAP--A Survey
## 4 Enhancements in CoAP
### Congestion Control Mechanisms in CoAP
#### 4.1.8 Cacc  
Context-Aware Congestion Control (CACC) proposed in [25] tackles the problem of differentiating the scenario of packet loss due bit error rate and congestion. It identifies the correct RTT of retransmitted message ACK by considering the dynamic network conditions. It comprises of three RTT estimators; weak RTT estimator, strong RTT estimator and failed RTT estimator. The strong and failed RTT combined together represent the successful deliver and packet dropping where the strong RTT is calculates in successful delivery and failed RTT is in the case of packet dropping. This scenario highlights the chance of link level packet collision, since some packets are delivered while rest are dropped. On the other hand, the high weak RTT value represents node level congestion delay. The method also restricts the RTO shrinkage in order to avoid negative variation in RTT which causes spurious retransmissions. In addition to this, the CACC also considers the retransmission count (RC) information in message transmission/retransmissions which enables it to accurately detect the weak RTT and smoothed RTT values. Finally, it incorporates the RTO aging mechanism for both small and large RTO estimations to avoid false RTO values in several network conditions. This aging technique is based on the CoCoA+ mechanism and it waits for the CACC to eventually increase the performance after RTO value is set to default in aging mechanism. This causes additional delays while waiting for CACC to increase performance over time. Moreover, the RTTVAR variable tends to vanish in case when sequence of similar RTTs is samples. This causes RTO values to get close to measure RTT. Also, in case of bursty traffic, neither the small weight of weak RTT (K = 1), not the avoidance of weak estimator is beneficial. Furthermore, the RTO value may increment steeply due to lack of aging mechanism of weak RTO.
# Band Gap Analysis for Materials with Cookie-Shaped Auxetic Microstructures, Using Finite Elements
## 3 Materials and Methods
### Applying Bloch Theorem on the Model  
To study a wave that propagates on a plane, the displacement \(q(r_{i})\) of the mesh elements is defined by the following equation:  
\[q\big{(}r_{j}\big{)}=q_{j}e^{(i\omega t-kr_{j})} \tag{4}\]  
where \(q_{i}\) denotes the wave width, \(\omega\) is the frequency, and \(k\) the wave vector. According to the Bloch theorem, there are two parameters, n\({}_{1}\) and n\({}_{2}\), that can be used for the recognition of each cell, so that the displacement can be found by only one combination of \(n_{1}\) and \(n_{2}\):  
\[q=q\big{(}r_{j}\big{)}e^{k(r-r_{j})}=q\big{(}r_{j}\big{)}e^{(k_{1}n_{1}+k_{2} n_{2})} \tag{5}\]  
with \(k_{1}=k\epsilon_{1}=\delta_{1}+i\epsilon_{1}\), \(k_{2}=k\epsilon_{2}=\delta_{2}+i\epsilon_{2}\), where \(\delta_{1}\), \(\delta_{2}\) is the damping constant and \(\epsilon_{1}\), \(\epsilon_{2}\) the phase constant.  
For the degrees of freedom \(q\) of a rectangular segment with edges \(L_{x}\), \(L_{y}\):  
\[q=\left[q_{1}^{T}q_{2}^{T}q_{3}^{T}q_{4}^{T}\right] \tag{6}\]  
With \(q_{i}\) we represent the nodal degrees of freedom, see Figure 2.  
As a wave propagation can be described as a Bloch wave, the relation between the periodic displacements \(q\) at the edges of the periodic element is:  
\[q_{2}=\lambda_{x}q_{1},\ q_{3}=\lambda_{y}q_{1},\ q_{4}=\lambda_{x}\lambda_{y }q_{1}\ \text{with}\ \lambda_{x}=e^{-i\mu_{x}},\ \lambda_{y}=e^{-i\mu_{y}} \tag{7}\]  
where constants \(\mu_{x}=\kappa_{x}L_{x},\ \mu_{y}=\kappa_{y}L_{y}\), describe the wave propagation.  
The nodal degrees of freedom can then be written as:  
\[q=\Lambda_{R}q_{1},\ \text{with}\ \Lambda_{R}=\left[I\lambda_{x}I\ \lambda_{y}I\ \lambda_{x}\lambda_{y}I\right] \tag{8}\]  
\begin{table}
\begin{tabular}{|c|c|} \hline \multicolumn{3}{|c|}{**Cartesian Coordinates**} \\ \hline \(\Gamma\) & (0,0) \\ \hline X & (L\({}_{1}\)/2,0) \\ \hline M & (L\({}_{1}\)/2, L\({}_{1}\)/2) \\ \hline \end{tabular}
\end{table}
Table 1: Coordinates used for the Irreducible Brillouin Zone, see Figure 1b.  
Figure 2: Rectangular segments with four nodes.  
Replacing \(q\) with \(q=\Lambda_{R}q_{1}\) in the equation \(\big{(}-\omega^{2}M+i\omega C+K\big{)}q=f\) and multiplying with \(\Lambda_{L}=\Big{[}I\lambda_{x}^{-1}I\ \lambda_{x}^{-1}I\ \big{(}\lambda_{x}\lambda_{y}\big{)}^{-1}I \Big{]}\) we have:  
\[\Big{(}-\omega^{2}\overline{M}\big{(}\mu_{x},\mu_{y}\big{)}+i\omega\overline{ C}\big{(}\mu_{x},\mu_{y}\big{)}+\overline{K}\big{(}\mu_{x},\mu_{y}\big{)} \Big{)}q=f \tag{9}\]  
with \(\overline{M}=\Lambda_{L}M\Lambda_{R}\), \(\overline{C}=\Lambda_{L}\Gamma\Lambda_{R}\), \(\overline{K}=\Lambda_{L}K\Lambda_{R}\) we represent the damping and stiffness matrixes  
Then, the eigenvalue problem becomes:  
\[\overline{D}\big{(}\omega,\lambda_{x},\lambda_{y}\big{)}=0\]  
where \(\overline{D}\) is the reduced matrix of the dynamic stiffness.
# Deciphering Autonomous Vehicle Regulations with Machine Learning
## 3 Methodology
### Feature Ranking  
This research adopted the Shapley values (SHAP) from cooperative game theory to fairly assign a value to each feature based on their contribution toward the prediction. The calculation accounts for the interaction effects between features by considering all combinations of features and how the addition of each feature contributes to the change in prediction. Hence, a SHAP value is the average marginal contribution of a feature value across all combinations such that  
\[\phi_{j}=\sum_{S\subseteq F\setminus\{j\}}\frac{|S|!(|F|-|S|-1)!}{|F|!}\Big{[} f_{S}\Big{(}X_{S\cup\{j\}}\Big{)}-f_{S}(X_{S})\Big{]} \tag{6}\]  
where \(\phi_{j}\) is the SHAP value for feature \(j\), \(F\) is the set of all features, \(S\) is a subset of features excluding \(j\), \(|S|\) is the number of features in subset \(S\), \(|F|\) is the total number of features, \(f_{S}\Big{(}X_{S\cup\{j\}}\Big{)}\) is the model prediction with feature \(j\) included, and \(f_{S}(X_{S})\) is the model prediction without feature \(j\). The sum is across all the subsets of features that exclude \(j\). Features with higher absolute SHAP values have a greater impact on the model's output.
## 2 Materials and Methods
### Spectral Pre-Processing and Reflectance Analysis  
Before model calibration we performed several pre-processing configurations using the Savitzky-Golay filter in order to remove both additive and multiplicative effects in the spectral data [24]. This digital filter smooths the data while the original characteristics are minimally affected [30,31]. Input of three parameters are needed: window size, polynomial order, and derivative order e.g., (5, 1, 1). Window size must be set in the form 2M + 1, where M is half of the window size [30]. This value is the number of consecutive points that will be used by least-squares smoothing, and for the end points of the curve this window can be asymmetric. If derivative order is given, the data is transformed. In the original publication, a detailed explanation of this filter can be found [25]. We also evaluated two additional functions, the Log(1/R) (where R is reflectance) to transform reflectance (R) into apparent absorbance [5], and mean-center function (centering) making the spectral curves to fluctuate around zero [32]. We carried out fourteen pretreatments to the spectral data including smooth and transformation by first and second derivative orders in the Savitzky-Golay algorithm.  
Spectral data can be decomposed into a small number of explanatory variables containing huge amount of variance [33]. Thus a principal component analysis (PCA) was performed on the spectral data to examine its structure and identify outliers.  
The relationship between soil carbon content and reflectance is essentially inverse, i.e., when soil carbon increases the reflectance decreases across the spectra [19,34,35]. To prove this postulation, we conducted an analysis for each soil type in order to observe the influence of soil carbon content on reflectance. Thus, the spectral curves corresponding to each sampled depth were averaged and plotted for visual analysis. In the same way, TC values were also averaged to be related with the spectral reflectance (Table 1).
# Effect of Strain Hardening on Increase in Collapse Pressure during the Manufacture of ERW Pipe
## 3 Evaluating Collapse Pressure
### Comparison with Design Standards  
Figure 14 compares the results of the collapse analysis with the design criteria recommended by two design standards: _API Recommendation Practice 111_[23] and _Offshore Standard DNV-OS-F101_[11]. Both standards use a combination of the elastic and inelastic collapse pressure to make recommendations for the collapse pressure of offshore pipelines. Both standards define the elastic and inelastic collapse pressures as identical, but the equations determining the design collapse pressure and modification factors are different. For example, _DNV-OS-F101_[11] introduces a fabrication factor to deduct the inelastic collapse pressure by 7% by considering the effect of changing material properties due to cold-forming. The difference between the two design standards was small for a large \(D/t\) where the elastic buckling is dominant. At a \(D/t\) of 17 or less, however, _DNV-OS-F101_[11] suggests a slightly lower design collapse pressure.  
Figure 13: Correlation according to sizing ratio (diameter-to-thickness ratio (\(D/t)=14\)) between: (**a**) collapse pressure and ovality; (**b**) compressive yield strength and collapse pressure.  
The comparison indicates that the minimum collapse pressures obtained in the simulation of the manufacture process approximated the design criteria for a larger \(D/t\). There was a difference of approximately 25%, however, between the minimum collapse pressure obtained from the simulation and that of the design criteria for a \(D/t\) of 14. This is because the current design standards do not factor in the advantage from the increased slope of the strain hardening that is caused by the sizing process. Thus, a larger margin exists for a smaller \(D/t\), for which inelastic buckling governs the collapse pressure of the ERW pipe.
## 3 Results and Discussion
### MFA Analysis  
MFA can be used to study relationships by simultaneously analyzing the observations, variables, and tables [51]. In this study, MFA was performed to analyze the relationship among five factors: E-nose, E-tongue, FAA, GC-MS, and fermentation time. After the analysis data were processed by dimensionality reduction, the first two factors had 65.88% (F1 and F2: 65.88%) of the variability. As shown in Figure 4, the points of each sample were projected onto the plane, and their observations were connected. The farther the observation point from the center, the better the observation point properties of the sample could distinguish it. Figure 4 shows that the E-nose aroma value of fermented pork loin ham with different fermentation time might be the key factor in distinguishing fermented pork loin ham with different fermentation times.  
The RV coefficients for the correlation of variables obtained with the E-tongue, GC-MS, E-nose, and FAA in eight selected samples are shown in Table 3. It is beneficial for the dimension with the most significant explained variance. The RV coefficients had values ranging from 0 to 1, making them easy to analyze. RV coefficients >0.7 indicated a good level of differentiation [52]. The two closest tables were the E -tongue and the FAA. This indicated that the FAA in the fermented pork loin ham highly correlated with the sensory scores brought by the taste.
# Just-in-Time Fluid Flow Simulation on Mobile Devices Using OpenVisFlow and OpenLB
## 2 Method
### Concept and Realization
#### 2.4.2 Structure of the _OpenLB_ Shared Library  
In this section, we present an overview of the _OpenLB_ shared library structure that we employed for performing the simulations on smartphones. Specifically, Algorithm 1 illustrates the main loop of the library, which adheres to the standard Lattice Boltzmann simulation structure with _OpenLB_. Initially, _OpenLB_ is instantiated, and crucial classes are initialized. The unit converter, which stores the lattice relevant data, is then declared. Next, the simulation domain is instantiated, and its dimensions correspond to the domain scanned with the smartphone. This domain is then passed to the load balancer, which distributes the cuboid into subsections necessary for parallel computing. The material number map is an array of numbers that refer to the materials in the simulation. However, it cannot be utilized as is and necessitates a transfer to the _OpenLB_-specific class, superGeometry. After preparing the geometry, the lattice is ready for simulation, and boundary conditions can be set. In particular, we apply the Smagorinksy BGK model in Section 2.2 to the material numbers of the fluid outflow and inflow. Additionally, we utilize the fringe region technique around the outflow. Moreover, we need to specify which material numbers define the inlet and outlet. We also add the postprocessor, which receives relevant dimensions and pointers to the result arrays. At step 10 of Algorithm 1, the simulation commences with the  
Figure 2: Communication between the application, _OpenCV_, and _OpenLB_.  
for-loop. In this loop, \(T\) corresponds to the total simulation time, while \(iT\) represents the current timestep. For each timestep, we update and set the boundary values. Subsequently, we call the collide and stream functions to retrieve the values for the subsequent step. Finally, we synchronize the number of timesteps saved per second with the frames per second (\(t_{fps}\)) of the smartphone application (step 13 to 21). This synchronization ensures better performance as we do not save every timestep of the simulation but still maintain a fluid visualization for the user.  
```
1:init OpenLB
2:declare unit converter
3:instantiation of the simulation domain
4:instantiation of a load balancer
5:preparing of the geometry
6:preparing of the lattice
7:add postprocessor
8:calculate the number of timesteps from the total simulation time \(T\)
9:start timer \(t_{fps}\)
10:for\(iT=0\); \(iT\leq T\); \(i++\)do
11: set boundary values
12:collide and stream
13:if\(t_{fps}\leq 1\)then
14:if\(\Delta t\leq 1/fps\) & \(count\leq fps\)then
15: write results via postprocessor
16: write Mega Lattice Updates per Second
17:endif
18: count++
19:endif
20: reset \(t_{fps}\)
21: count = 0;
22:if endSimulation then break;
23:endif
24:endfor
```  
**Algorithm 1** Mainloop of the _OpenLB_ Shared Library
## 5 Experimental Setup
### Experiments Design
#### 5.3.1 Pattern Recognition Validation  
The initial phase focused on evaluating the system's pattern recognition capabilities across the previously defined wave categories. This validation process involved identification of established wave patterns in historical data, coupled with verification of agents' analyses.
# Real-Time Indoor Positioning Based on BLE Beacons and Pedestrian Dead Reckoning for Smartphones
## 3 Methodology
### PDR Positioning
#### 3.3.3 Heading Direction Estimation
\tag{13}\]  
To simplify the description, let \(\mathbf{C}_{n}^{b}= \begin{bmatrix}C_{11}&C_{12}&C_{13}\\ C_{21}&C_{22}&C_{23}\\ C_{31}&C_{32}&C_{33}\end{bmatrix} \), where \(C_{11},\ldots,C_{33}\) correspond to the 9 elements of the matrix in (13). The quaternion method is used to calculate the heading angular. Let the quaternion vector be \(\mathbf{Q}= \begin{bmatrix}q_{0}&q_{1}&q_{2}&q_{3}\end{bmatrix} ^{\mathsf{T}}\). The initial value is given by  
\[\mathbf{Q}= \begin{bmatrix}q_{0}\\ q_{1}\\ q_{2}\\ q_{3}\end{bmatrix} = \begin{bmatrix}\frac{1}{2}\sqrt{1+C_{11}+C_{22}+C_{33}}\\ \frac{1}{2}\sqrt{1+C_{11}+C_{22}-C_{33}}\\ \frac{1}{2}\sqrt{1-C_{11}-C_{22}+C_{33}}\end{bmatrix} . \tag{14}\]  
The gyroscope samples the 3-axis angular velocity \(\omega= \begin{bmatrix}\omega_{x}&\omega_{y}&\omega_{z}\end{bmatrix} ^{\mathsf{T}}\) every \(\Delta t\) time. Then the quaternion is updated using the first-order Runger-Kutta method as follows:  
\[\mathbf{Q}_{t+\Delta t}=\bigg{(}\mathbf{I}\cos(\Delta\theta/2)+\Omega(\omega) \Delta t\frac{\sin(\Delta\theta/2)}{\Delta\theta}\bigg{)}\mathbf{Q}_{t}, \tag{15}\]  
where \(\mathbf{I}\) is a \(4\times 4\) identity matrix, \(\Delta\theta=\Delta t\sqrt{\omega_{x}^{2}+\omega_{y}^{2}+\omega_{z}^{2}}\), \(\Omega(\omega)= \begin{bmatrix}0&-\omega_{x}&-\omega_{y}&-\omega_{z}\\ \omega_{x}&0&\omega_{z}&\omega_{y}\\ \omega_{y}&-\omega_{z}&0&\omega_{x}\\ \omega_{z}&\omega_{y}&-\omega_{x}&0\end{bmatrix} \). Then the estimated heading angle \(\varphi_{g}\) can be calculated by:  
\[\varphi_{g}=\arctan\Bigg{(}\frac{2(q_{1}q_{2}+q_{0}q_{3})}{q_{0}^{2}-q_{1}^{2 }+q_{2}^{2}-q_{3}^{2}}\Bigg{)}. \tag{16}\]  
By substituting (12) and (16) into (8), the fused heading angle \(\varphi\) is finally obtained.
# Credibility Based Imbalance Boosting Method for Software Defect Proneness Prediction
## 6 Discussion
### Is CIB Applicable for Cross-Project Defect Prediction?  
Honestly, we do not suggest directly using CIB for cross-project defect prediction (CPDP), because CIB is designed for addressing class-imbalance problem in within-project defect prediction (WPDP). As we all know that WPDP is completely different from CPDP. Therefore, the methods for WPDP are not suitable for addressing CPDP task. The biggest challenge in CPDP is the distribution difference between source and target datasets. CIB should be modified before using for cross-project defect prediction. To this end, for example, we can modify the method for calculating credit factor of synthetic minority class samples.  
Figure 5: Boxplot of \(F_{1}\), AGF, AUC, and MCC across all NASA datasets for our CIB and the baselines. The horizontal line in the box denotes the median, the asterisk indicates the mean value, and the red cross represents outliers.  
The more the similarity between synthetic sample and the target dataset is, the bigger the credit factor of this synthetic sample. We plan to design a specific method to implement our idea in the future work.
## 3 The Proposed Method
### Numerical Scheme  
In this paper, the spatial domain \(\Omega\) is discretized by a grid domain \(\{(x_{i},y_{j}),x_{i}=ih_{t},y_{j}=jh,i=0,1,2,\cdots,n_{x}-1,j=0,1,2,\cdots,n_{y}-1\}\) and the time interval \((0,T)\) is discretized as \(t_{n}=n\tau,n=0,1,2,\cdots,n_{t}-1\). Then the numerical solution of \(I,v\) at \((x_{i},y_{j},t_{n})\) can be denoted as \(I_{ij}^{n},v_{ij}^{n}\).  
Apply the minmod scheme [1]\(m(a,b)=\frac{sgn(a)+sgn(b)}{2}\min(|a|,|b|)\) to approximate the TV diffusion terms, and apply central difference to approximate all other differential term, then an efficient numerical scheme for the proposed model (14) can be denoted as  
\[\left\{\begin{array}{l}\dfrac{I_{ij}^{n+1}-I_{i,j}^{n}}{I_{i,j}^{n}}=TV_{m}( I)_{i,j}^{n}+\beta TV_{m}(I+v)_{i,j}^{n}-\lambda_{I}^{n}(I_{ij}^{n}+v_{ij}^{n}-f_{ ij}^{n}),\\ \dfrac{v_{ij}^{n+1}\Upsilon^{-}v_{ij}^{n}}{\tau}=\dfrac{\Delta_{+}^{x}-\Delta _{-}^{x}+\Delta_{+}^{y}-\Delta_{-}^{y}}{h^{2}}+\beta TV_{m}(I+v)_{i,j}^{n}- \lambda_{v}^{n}(I_{ij}^{n}+v_{ij}^{n}-f_{ij}^{n}).\end{array}\right. \tag{17}\]  
Here, \(TV_{m}(I)i,j^{n}\) is denoted as  
Figure 1: Traditional result and our result on artificial image \(1\).  
\[TV_{m}(I)^{n}_{i,j} =\frac{1}{h}\Bigg{\{}\Delta^{x}_{-}\left[\frac{\Delta^{x}_{+}I^{n}_{ i,j}}{\sqrt{(\Delta^{x}_{+}I^{n}_{ij})^{2}+m(\Delta^{y}_{+}I^{n}_{i,j}\Delta^{y}_{-}I^{n }_{i,j})}}\right]\] \[+\Delta^{y}_{-}\left[\frac{\Delta^{y}_{+}(c^{n}_{i,j}\cdot u^{n}_{ i,j})}{\sqrt{(\Delta^{y}_{+}u^{n}_{i,j})^{2}+m(\Delta^{x}_{+}u^{n}_{i,j}\cdot \Delta^{x}_{-}u^{n}_{i,j})}}\right]\Bigg{\}}. \tag{18}\]  
and the Lagrange multipliers can be determined by  
\[\lambda^{n}_{I}=\frac{\sum_{i,j}\Bigl{[}TV_{m}(I)^{n}_{i,j}+\beta TV_{m}(I+v)^ {n}_{i,j}\Bigr{]}\bigl{(}I^{n}_{i,j}+v^{n}_{i,j}-f^{n}_{i,j}\bigr{)}}{\sum_{i, j}(I^{n}_{i,j}+v^{n}_{i,j}-f^{n}_{i,j})^{2}} \tag{19}\]  
or  
\[\lambda^{n}_{v}=\frac{\sum_{i,j}\Bigl{[}\frac{\Delta^{x}_{+}-\Delta^{x}_{-} +\Delta^{y}_{+}-\Delta^{y}_{-}}{h^{2}}+\beta TV_{m}(I+v)^{n}_{i,j}\Bigr{]} \bigl{(}I^{n}_{i,j}+v^{n}_{i,j}-f^{n}_{i,j}\bigr{)}}{\sum_{i,j}(I^{n}_{i,j}+v^ {n}_{i,j}-f^{n}_{i,j})^{2}}. \tag{20}\]  
After discretization of the boundary condition and initial condition by the central difference scheme, the numerical algorithm can be implemented efficiently.
## 4 Discussion  
### Effect of Lubrication  
Figure 2 compares the developments of the COF for the ASF samples as a function of sliding distance under dry and lubricated conditions. During lubricated sliding wear, each curve shows an initial increase in the COF as the track is worn, followed by a slight drop and then almost immediately a steady COF with increasing sliding distance. The steady state is achieved sooner than in the dry sliding tests, due to the lubricant film present causing a slipping action on the surface of the sample, leading to a faster settling of the COF. There is a significant decrease in the COF when compared to the dry sliding tests (Table 2). The values for different particle sizes are similar as opposed to being evidently different from one another under dry conditions. This is because the lubricant effectively dictates the wear behaviour and therefore negates the magnitude of the particle size effect on the COF. The marginal difference in the COF as particle size changes can be attributed to the fact that there are more ASW particles present in the smaller particle size samples.  
The micrographs of the worn surfaces of the ASF samples after the lubricated sliding wear tests (Figures 3-5) show that the wear tracks for all samples are shallower than their counterparts seen under the dry conditions. The surfaces of the wear tracks are also more even and show little presence of metal. This is because the lubrication on the surface of the sample causes a slipping action that reduces the amount of wear occurring. Some unbroken porous particles are observed on the surfaces of the wear tracks of the L-ASF and M-ASF samples, as indicated by arrows in Figures 4 and 5. They can act as reservoirs and provide a constant distribution of the lubricant, which may also contribute to the lower COF and specific wear compared to the dry sliding wear conditions.  
The micrographs of the static partners for the S-ASF and M-ASF samples (Figures 3 and 4) show a significant presence of abrasive wear after the lubricated sliding wear tests. This is different from the dominant adhesive wear during the dry sliding wear tests. There is also no evidence of delamination or oxidative wear. The change in wear type can be attributed to different lubricating effects on the metal matrix and on the porous ceramic particles. Although the lubricant prevents excessive wear due to its slipping action, it does carry debris lost from the ceramic particles, which cause abrasion. This debris can deposit onto the surface of the static partner [33; 34; 35; 36].