esa-sceva/satcom-chunk-collection huggingface dataset:
# A QoS Optimization Approach in Cognitive Body Area Networks for Healthcare Applications
## 3 System Model
### Channel Allocations
#### 3.5.1 Reinforcement Learning--Channel Assignment Algorithm (RL-CAA)
Low values of \(\beta\) assures enough memory of the past rewards. Decreasing the learning rate \(\alpha\) with the RL steps improves the convergences speed of the algorithm. Thus, the learning rate is linearly decreased as given in Equation (10):  
\[\alpha(\mathrm{t})=\alpha(\mathrm{t}-1)-\Delta \tag{10}\]  
where \(\Delta\) should be small enough to assure a smooth transition between steps and negative values for \(\alpha\) should be avoided. If any channel is giving a throughput below the target throughput, or if the channel is experiencing co-channel interference, the reward function returns zero (Equation (4)) and the RL-CAA discards that channel and continues its exploration. Once the algorithm has converged, the set of optimal channels is known (Equation (11)): duplicated number  
\[\text{Y}=\{\text{y}_{1},\text{y}_{2},\dots\text{y}_{\text{M}}\} \tag{11}\]  
where \(\text{y}_{\text{i}}\) represents the outcome of the RL-CAA for an individual channel. These channels are subsequently assigned to the sensor nodes and the gateway starts receiving data from the nodes. At any instant, due to the change in throughput requirements (either the target throughput has been changed or the traffic load has changed in the zone due to handovers) the particular gateway executes the RL-CAA to look for the new optimal channel allocations to satisfy its governed nodes. Some of the key parameters of the RL-CAA are presented in the Table 3. The key steps of the RL-CAA are listed in Algorithm 1 (note that this is a revised version of [34] with more focus on QoS requirements and co-channel interference). ```
1.REPEATUNTILRLConvergenceORRLEvaluationsteps
2.Sense the available channels' conditions, i.e., SNR, interference
3.Calculate instantaneous reward, r(t), as per Equation (4)
4.Update average reward, \(\bar{\text{r}}(\text{t})\), as per Equation (9)
5.FORi=1 toM
6.Update: x, w, p
7.ENDFOR
8.FORi=1 toM
9.IFp!=0.5
10.y!=1, i.e., assign the channel
11.ELSE
12.y!=0, i.e., do not assign the channel
13.ENDIF
14.ENDFOR
15.ENDREPEAT
16.FORi=1 toM
17.C!=Channel_Capacity(y!) 18.IFTotalTH!=Req_TH
19.TotalTH!=TotaITH+C!=20 ELSE
21.y!=0
22.ENDIF
23.ENDFOR
```  
**Algorithm 1**The key steps of the RL-CAA.
## 3 Results and Discussion
### Machine Learning to Predict \(q_{u}\) and \(E_{t}\)  
Initially, several established explicit models such as linear regression, Lasso, Ridge, and GMDH were utilized. The corresponding equations and associated metrics are summarized in Table 7. Notably, the GMDH model yielded the most favorable metrics, indicating its potential for generating reliable results. However, due to the lengthy equations involved, we opted to integrate the GMDH features with the most effective implicit model, namely ANN, as elaborated in the subsequent section. This combined model is denoted as ANN-GMDH. Lastly, a user-friendly program will be developed to facilitate straightforward predictive tasks.  
Figure 15: Unconfined compression tests on mixtures corresponding to the optimal moisture and maximum dry density according to standard Proctor’s tests: (**A**) the change in \(q_{u}\) as a function of \(Fs\), (**B**) the change in \(E_{t}\) as a function of \(Fs\).
# A Robust Wind Turbine Component Health Status Indicator
## 2 Materials and Methods
### The Gaussian Mixture Copula Model
#### 2.5.1 Estimation of the GMCM Parameters
Convergence  
Convergence is indeed an essential issue in optimizing the model, and there is no guarantee of convergence as said before and discussed in [52]. Therefore, both methods (NM and PEM) have been tested to fit the GMCM model. To do so, 50 different starting parameters have been iterated to ensure the model's convergence in each optimization method and obtain a successful fitting. It is remarkable that we are dealing with, a seven-variable model, and no convergence was obtained in any case when the optimization method is PEM. However, the NM method achieves successful fittings in all cases in a reasonable time, with the convergence time ranging from 65 to 232 s in the analyzed trials. As an example of these tests, the convergence times for model fitting in four of the cases from site 2 are provided in Table 6.  
This is consistent with the findings reported in [52], where the NM method was already selected as the most robust among those tested. The fitting algorithms were executed on a machine with the following characteristics: Processor Intel(R) Core(TM) i58250U CPU @1.6 GHz, 16 GB RAM. Identifiability of the Parameters  
The GMCM suffers from unidentifiable parameter configurations due to its invariance properties. For example, the translation invariance implies that only the relative distances between the location parameters, \(\mu_{1},\ldots,\mu_{m}\), can be deduced. This is (partially) addressed by arbitrarily anchoring the first component to \(\mu_{1}=0\). To account for scaling invariance, the variance of the first component is scaled to unity in each dimension, \(\Sigma_{1 kk}\) for all \(k\). Nevertheless, problems of identifiability may still appear in specific scenarios.
## 4 Results and Discussions
### The Stick-Slip Torque  
In metal deep drilling, when the chips were stuck inside the flutes, a cumulative resistant torque occurred and thus required an increase of consumption torque. When the active torque is higher than the resistance, the chips slipped inside the hole wall, and resulted in a quick reduction of the torque. These arbitrary changes can be clearly observed in Figures 4 and 5. Since this component is unsystematic, a statistical analysis was applied. The maximum values of \(T_{3}\) of each of the investigated tests were taken and then statistically inferred, providing probabilistic distributions of the population data for \(T_{3}\) as depicted in Figure 5. As can be seen in the Figure, the mean value of the maximum stick-slip torques in UADs was significantly smaller than that in the CDs.  
In order to double confirm and quantitatively carry out the effectiveness of ultrasonic assisted drilling over the conventional drilling in the same cutting parameters, a paired \(t\)-test was made by comparing the \(T_{3}\) values in pairs. The average difference, \(\mu\)_difference, was calculated as:  
\[\mu\text{\_difference}=T_{3\_CD}-T_{3\_UAD} \tag{10}\]  
Table 7 presents the paired \(t\)-test results. With a confidence level of 95% and the \(p\)-value of 0.012, it can be said that the stick-slip \(T_{3}\) in the UADs tests was smaller than that in CDs.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \(\mathbf{a_{0}}\) & \(\mathbf{a_{1}}\) & \(\mathbf{a_{2}}\) & \(\mathbf{a_{3}}\) & **Statistics Reduced Chi-Sqr** & **Statistics Adj. R-Square** \\ \hline CD & \(-13.7214\) & \(1.8805\) & \(-4.9359\) & \(0.6979\) & \(0.0026\) & \(0.99995\) \\ \hline UAD & \(15.3907\) & \(-2.2047\) & \(6.0016\) & \(-0.8265\) & \(0.0141\) & \(0.99975\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Cutting torque \(T_{1}\) from CD and UAD experiments.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Mean** & **StDev** & **SE Mean** & **95\% Lower Bound for \(\mu\)\_difference** & \(t\)**-Value** & \(p\)**-Value** \\ \hline
0.1190 & 0.0580 & 0.0137 & 0.0952 & 2.12 & 0.024 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Paired \(t\)-test for difference of the chip-evacuation coefficient, A between CDs and UADs.  
It can be seen that the maximum values of \(T_{3}\) as well as its distribution rule appeared as stochastics factors. Hence, further studies are required to complete the model to predict the stick-slip torque, \(T_{3}\).
## 3 Materials and Methods
### Methods
#### 3.2.1 Data Preprocessing  
Due to different spatial and temporal resolutions (Table 1), the described data needed preprocessing before input into the DCNN. The workflow is shown in Figure 2 and described in this section in more detail.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Definition** & **Area (ha)** & **Area (\%)** \\ \hline thinning 1 & Forest, thinning within 0–3 years & 958.95 & 9.3 \\ thinning 2 & Forest, thinning within 3–10 years & 1404.88 & 13.6 \\ no thinning & Forest, no thinning & 7245.62 & 70.3 \\ other & Non forest (buildings, roads, water bodies) & 696.19 & 6.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Reference data classes, their definitions, and occupied area.  
Figure 2: Data preprocessing workflow with all data manipulation steps (see text).  
The provided canopy height model (CHM) contained values ranging from \(-\)73.9 to 138. Since values below 0 are physically impossible and tree heights above 45 m are improbable in Austria, these values need adjustment. Therefore, values below 0 and over 40 m were highlighted, visually inspected, and classified in GIS. The analysis showed that highlighted pixels higher than 47 m and negative values were predominantly rocky steep slopes. Consequently, all pixels with a value below 0 and above 47 m were set to 0.
# A Survey of Data Representation for Multi-Modality Event Detection and Evolution
## 7 Datasets and Evaluation
### Evaluation
Therefore, filter non-event data in advance can effectively improve the event detection performance. From the results, we can see that, there is still space for improvements of the multi-modality event detection tasks. As shown in Table 8, seven methods are compared and evaluated. In which SLDA (visual) and SLDA (text) are the supervised Latent Dirichlet Allocation (SLDA) [136] with only visual features and textual features, respectively. mmLDA + SG and mmLDA + SVM are multi-modal Latent Dirichlet Allocation (mmLDA) [137] using softmax regression method and Support Vector Machine (SVM) for classification, respectively. mm-SLDA [97] is a supervised version of mm-LDA [137]. BMM-SLDA [117] is boosted multi-modal supervised Latent Dirichlet Allocation that used boosting weighted sampling strategy to train the corresponding topic models. KGE-MMSLDA [118] is the knowledge priors- and max-margin-based topic model that integrates additional knowledge from an external knowledge base. Table 8 shows the performance on multi-modality event datasets (MMSE and HUFT-mmdata) and the accuracy values. In which, the bold number represents the best results of methods under different multi-modality social event dataset. It is obvious that the performance of SLDA (text) is much better than that of SLDA (visual) on all datasets, which means that the textual information is more important than visual information on multi-modality event detection tasks. In addition, SLDA (Text) is better than mmLDA (SG); this is because mmLDA only adopts the multi-modality information and SLDA uses the supervised information. It means that supervised information is useful. However, multi-modality based methods, such as mm-SLDA, BMM-SLDA and KGE-MMSLDA, achieved better results compared with the single-modality based methods such as SLDA (visual) and SLDA (text), which means that multi-modality feature is effective for event detection. In addition, results on HUFT-mmdata show that KGE-MMSLDA performs the best compared with other methods. It is because KGE-MMSLDA integrates additional knowledge into a unified topic model, which means that embedding external knowledge can effectively improve the event detection performance.
## 4 Stability Analysis of GFMC APF
### Impedance Modeling of GFMC APF  
Figure 4 presents the simplified control block diagram of the GFMC APF, highlighting its control and plant components. The voltage controller \(G_{\text{v}}(s)\) combines a PI controller and RC to achieve effective harmonic compensation. The combined controller can be represented as follows:  
\[G_{\text{v}}(s)=G_{\text{PI}}(s)+G_{\text{RC}}(s) \tag{7}\]  
The PI controller is responsible for regulating the fundamental component of the voltage and is defined as follows:  
\[G_{\text{PI}}(s)=K_{\text{vp}}+\frac{K_{\text{vi}}}{s} \tag{8}\]  
The repetitive controller is designed to suppress periodic disturbances caused by harmonics and is described as follows:  
\[G_{\text{RC}}(s)=\frac{K_{\text{r}}\cdot e^{-(N-K)sT_{\text{s}}}}{1-Q_{\text{ s}}\cdot e^{-NsT_{\text{s}}}} \tag{9}\]  
The current control loop is implemented with a proportional controller, represented by \(G_{\text{c}}(s)\), which is expressed as follows:  
\[G_{\text{c}}(s)=K_{\text{cp}} \tag{10}\]The current control loop also includes a current feedback filter, \(G_{\text{if}}(s)\), set to 1. This allows the current feedback to be directly utilized without additional filtering, ensuring precise and efficient control.  
The system also incorporates delays caused by duty ratio computation and pulse-width modulation (PWM) updates [23]. These delays are modeled as follows:  
\[G_{\text{d}}(s)=e^{-1.5T_{\text{s}}s} \tag{11}\]  
Here, \(T_{\text{s}}\) denotes the sampling period. In this article, the switching and sampling frequencies are chosen to be identical, resulting in an equivalent time delay of \(1.5T_{\text{s}}\).  
Finally, the plant model consists of the transfer functions of the passive components, which define the relationships between the control inputs and the physical dynamics. The transfer functions from the converter-side voltage \(v_{\text{gl}}(s)\) to the capacitor voltage \(v_{\text{gf}}(s)\) and the grid-injected current \(i_{\text{gg}}(s)\) are derived as follows:  
\[G_{\text{vgf}}=\frac{v_{\text{gf}}(s)}{v_{\text{gi}}(s)}=\frac{L_{\text{gg}}+ L_{\text{s}}}{s^{2}(L_{\text{gg}}+L_{\text{s}})L_{\text{gi}}\text{C}_{\text{gf}}+( L_{\text{gi}}+L_{\text{gg}}+L_{\text{s}})} \tag{12}\]  
\[G_{\text{igg}}=\frac{i_{\text{gg}}(s)}{v_{\text{gi}}(s)}=\frac{s^{2}(L_{\text{gg }}+L_{\text{s}})C_{\text{gf}}+1}{s^{3}(L_{\text{gg}}+L_{\text{s}})L_{\text{gi} }\text{C}_{\text{gf}}+s(L_{\text{gi}}+L_{\text{gg}}+L_{\text{s}})} \tag{13}\]  
The open-loop transfer function for the system will become  
\[G_{\text{ol}}=\frac{G_{\text{v}}(s)K_{\text{cp}}G_{\text{d}}(s)s(L_{\text{gg} }+L_{\text{s}})}{s^{3}(L_{\text{gg}}+L_{\text{s}})L_{\text{gi}}\text{C}_{\text {gf}}+s^{2}K_{\text{cp}}G_{\text{d}}(s)C_{\text{gf}}(L_{\text{gg}}+L_{\text{ s}})+s(L_{\text{gg}}+L_{\text{s}}+L_{\text{gi}})+K_{\text{cp}}G_{\text{d}}(s)} \tag{14}\]
## 2 V2P System Architecture  
A typical V2P crash prevention system involves periodic exchange of safety messages among vehicles and VRUs. This communication can happen either directly using ad-hoc communication technologies, such as IEEE 802.11p, or indirectly using infrastructure-based communication, such as, cellular technology. Also, V2P system performs its operation in three phases: detection, tracking and trajectory prediction, and action [14]. These elements have led to different V2P system architectures. In this section, we briefly discuss the different V2P architectural components and the safety messages.
# A Robust Mutual Authentication with a Key Agreement Scheme for Session Initiation Protocol
## The Proposed Scheme
### Forward Secrecy  
Forward secrecy means that all past session keys remain secure even though a server's master key is compromised by an attacker \(A\). In our proposed scheme, \(SK\)= \(h(J_{i}||T_{1}||T_{2})\) = \(h(r_{s}A_{i}R_{c}||T_{1}||T_{2})\) = \(h(r_{c}A_{i}R_{s}||T_{1}||T_{2})\). When \(A\) obtains the server's secret key \(k_{s}\), \(A\) can compute \(k_{s}R_{c}\). With this value, \(A\) can decrypt \(H_{i}\) to obtain \(T_{1}\),\(ID_{i}\) and decrypt \(L_{i}\) to obtain \(T_{2}\), \(R_{s}\). Furthermore, \(A\) can compute \(A_{i}=h(ID_{i}||k_{s})\). However, the values of \(r_{s}\) and \(r_{c}\) are out of the range of \(A\). Therefore, even if \(A\) knows the server's master key, \(A\) cannot know any past session keys. Therefore, our proposed scheme provides forward secrecy.
## 3 Results and Discussion
### H Atom Adsorption Sites of Mo-doped Nb (100) Surface  
Based on the slab model of Mo-doped Nb (100) surface, there are three possible adsorption sites for hydrogen atoms, i.e., top site (TS), bridge site (BS), and hollow site (HS), as shown in Figure 4. The adsorption energies (\(E_{ads}\)) are calculated to characterize the preferred location of the adsorbed hydrogen atom. When Nb doping is considered, \(E_{ads}\) can be expressed as  
\[E_{ads}=E(Nb_{n}Mo_{m-n}H)-E(Nb_{n}Mo_{m-n})-E(H) \tag{3}\]  
Here, \(m\) represents the total number of atoms, \(n\) represents the number of Nb atoms in the slab model, \(E(\mathrm{H})=-0.019\) eV, \(E(\mathrm{Nb_{n}Mo_{m-n}})\) and \(E(\mathrm{Nb_{n}Mo_{m-n}H})\) the total energies of the corresponding crystal structures. The calculated \(E_{ads}\) values are summarized in Table 2. Among the possible positions, the largest adsorption energy (\(-4.27\) eV) belongs to the hollow site on top of Nb atom (H1), which indicates that H1 sites are the preferential sites of adsorbed hydrogen atoms. In addition, the average vertical distance between adsorbed H atom and the top layer (\(d_{\mathrm{H-Surf}}\)) as well as the distance between H atom and TM atoms (\(d_{\mathrm{H-TM}}\) ) are also calculated and listed in Table 2. The bonding lengths (\(d_{\mathrm{H-TM}}\)) between the hydrogen-atom and TM atom show the descending on the hollow, bridge and top sites, respectively. The smallest \(d_{\mathrm{H-Surf}}\) (0.44 A) belongs to the H1 site, indicating that there exists a very strong interaction between H atom and (100) surface. Similar results were obtained by [PERSON] and co-authors [46].  
\begin{table}
\begin{tabular}{c c c c} \hline
**Site** & **E\({}_{ads}\) (eV)** & **d\({}_{\mathrm{H-TM}}\) (Å)** & **d\({}_{\mathrm{H-Surf}}\) (Å)** \\ \hline T2 & \(-3.41\) & \(1.77\) & \(1.85\) \\ T1 & \(-3.26\) & \(1.85\) & \(1.93\) \\ B2 & \(-3.77\) & \(1.97\) & \(1.30\) \\ B1 & \(-3.75\) & \(1.97\) & \(1.21\) \\ H2 & \(-4.24\) & \(1.98\) & \(0.46\) \\ H1 & \(-4.27\) & \(1.99\) & \(0.44\) \\ \hline \end{tabular}
\end{table}
Table 2: Calculated adsorption energy (\(E_{ads}\)), average distance between H atom and metal atom (\(d_{\mathrm{H-TM}}\)) as well as average distance between H atom and the top layer (\(d_{\mathrm{H-Surf}}\)) in slab model of Mo-doped Nb (100) surface for adsorbed hydrogen atoms at different sites indicated in Figure 4  
Figure 3: Work function along surface normal direction (Z-axis) for Nb\({}_{12}\)Mo\({}_{4}\) (100) surface model with seven atomic layers.
## 4 Discussion  
### Pilot Point Based Calibration Evaluation  
Through the calibration process, the estimation of the parameters gave the maximum optimized value of the hydraulic conductivity. The result shows a positive difference with a 0.901 R-squared for the 60 pilot points with MHMDG placement compared to 0.630 for the non-calibrated model when using the optimized parameters. The MHMDG 60 pilot points give a better performance than the regular placement of any density of parameters and minor change in standard deviation. Based on the [PERSON] theory, a concise statistical overview of the model result in terms of correlation, root-mean-square difference, and variance ratio is presented in a Taylor diagram to evaluate the model [48]. The observation and calculated values comparison show that the MHMDG placement model with 60 pilot points is the most stable and accurate calibration (Figure 9). Over the fifth iteration, the value of the parameters meets the stability conditions and the objective function. The methodology proposed here showed that the pilot-point-based calibration gives a sound approximate random output of parameters as an outcome and a close correlation between the observed data and the numerically simulated hydraulic head. The water table simulated by the MODFLOW model presents a minor variation compared to the water table interpolated from the observation data (Figure 5).  
The study's objective is to improve the technics of groundwater flow calibration based on pilot points placement through a scenario and repeated simulation experiment realized using the MODFLOW 2005 engine and the PEST suit. The frequency of pilot points influences the iteration time; the more the pilot-point density increases, the longer the computation time. However, the accuracy is increased with the density as well. The modeler must determine a balance during a manual calibration to minimize computing time and burden. The pilot point calibration method is more effective when combined with the zonation and placed between the observation wells and the boundary condition. The model simulation could not tolerate the gap of the pilot point in the model area; it must be filled with less density. With the MODFLOW 2005 engine and PEST suit, the model's three-dimensional and two-dimensional pilot point's influence is negligible.  
In the small-scale area, the kriging interpolation with exponential variogram applied here for the assignment for the pilot point value is exceptionally suitable as it approximates the solution with high accuracy. An excessive variogram might impact the iteration and parameters sensitivity and limit the objective function, which becomes stable and constant before around 90% of the simulation. In a high water level fluctuation region, a trendier change of parameters sensibilities occurs but is less sensible in the MHMDG 60 pilot points. The other placement design's sensitivity result does not follow any tendency (Figure 8).  
Figure 9: Taylor diagram of the comparison between the observed head and the calculated head.
## 2 MDD/GNSS Installations and Measurement Methods
### GNSS Configuration and Principle
The double-phase difference, \(\left(\phi_{mi}^{k}-\phi_{mb}^{k}\right)-\left(\phi_{mi}^{l}-\phi_{mb}^{l}\right)\), is obtained based on both two single-phase differences as follows:  
\[\phi_{mi-mb}^{k-l}=\frac{r_{mi-mb}^{k-l}}{\lambda}+N_{mi-mb}^{k-l}+\epsilon_{ mi-mb}^{k-l}+\frac{T_{\phi_{mi-mb}}^{k-l}}{\lambda} \tag{3}\]  
Therefore, Equation (2) is called the fundamental equation for relative positioning. According to Equation (2), the observation solution for relative positioning is obtained based on the left side which is observed by a GPS sensor. This means that the three-dimensional coordinates of the measurement point shown in \(r_{mi-mb}^{k-l}\) and integer ambiguity \(N_{mi-mb}^{k-l}\) are determined by means of the least squares method for residual \(\epsilon_{mi-mb}^{k-l}\). Thus, we used the relative positioning method with static measurement of two points to monitor slab track displacements because it yields the most reliable and precise measurement results in GNSS positioning at the top point of the track TCL layer. Thus, the top displacement of the TCL surface with time can be monitored after installation of GNSS receivers.
## 4 Results  
The results obtained from the data recorded by the multi-parametric monitoring system installed at the Acuto test-site for comparing the three aforementioned approaches are presented in the following sections. The data acquisition was carried out starting from April 2016, and it was split in two phases: Phase A, in which weather and strain monitoring were carried out by continuous recording to detect daily and seasonal changes in rock behaviour due to natural forcing actions, and Phase B, carried out from 18 th July to 21 st July 2016, in which the rock block was forced by dynamic actions to induce strain effects under controlled conditions.