esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### CFD Simulations
#### 2.3.2 Model Set Up and Boundary Conditions in CFD  
The steady state conditions were used in the CFD analysis of the single-phase airflow in the domain. The computational domain in this study had the dimension of L \(\times\) W \(\times\) H = 5.06 m \(\times\) 6.06 m \(\times\) 2.03 m. The distances from the scale model to the inlet, outlet, side walls (i.e., parallel to airflow), and the top of the domain were almost 19H\({}_{1}\) (H\({}_{1}\) = 0.107 m, height of the building), 24H\({}_{1}\), 23H\({}_{1}\), and 17H\({}_{1}\), respectively. It  
domain should be far enough away from the region of interest, and in our study, distances from each side from the region of interest were far bigger than the recommended minimum. These distances were chosen intentionally for studying the effects of surroundings and wind direction in the future study. Even though it would cost more meshes and calculation time, there will not be any sidewalls effect on the airflow.  
Velocity inlet was used for upstream boundary condition. The inlet air velocity profile was calculated by Equation (1) using the same reference value \(U_{\textit{ref}}\) and \(Z_{\textit{ref}}\) as the wind tunnel experiment, which represents boundary layer airflow for an open field. The turbulence intensity profile of the wind tunnel was followed the Equation (2). Turbulent intensity (fraction value) profile was assigned with an expression (ANSYS CFX) following Equation (2) along with turbulence length scale of 0.05 m; roughly representing the height of roughness elements in the BLWT. Wall boundary was used for the walls of the scale model, and the top and the sides of the computational domain. The sand grain roughness height of \(2.5\times 10^{-4}\) m was used for the ground surface in the direct vicinity of the scale model, which was equivalent to the surface roughness of the wind tunnel. The top of the computational domain was modeled as a free slip wall (zero normal velocity and zero normal gradients of all variables). The other wall boundaries were set as a no-slip smooth wall. Pressure outlet with zero static pressure was imposed at the outlet boundary.  
For various wind speed investigations, the inlet velocity U was set to 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10-m\(\cdot\)s\({}^{-1}\), respectively for a single opening scenario (Case 1, Table 1). Eight opening scenarios and 10 velocities resulted in a total number of 18 simulations. The effect of different opening scenarios (Table 1) on airflow pattern and airflow rate were simulated and investigated using the wind tunnel inlet velocity 8 m\(\cdot\)s\({}^{-1}\) which is correspondence the reference velocity \(U_{\textit{ref}}\) 4.41 m\(\cdot\)s\({}^{-1}\). The other boundary conditions remained the same as described before.
## 5 Case Study B: Underwater OCC under MIMO Diversity
Similar to the model from the empirical study, the model was trained for 50 epochs with all the same parameters and hyperparameters, i.e., no hyperparameter searching. Refer to Figure 4 for details of the DNN architecture. After training the model, the model was validated against the remaining 40% of the dataset and the results are presented in Figure 10. As can be observed, when the model trains on the specific dataset that it will be validated against, it performs much better with an overall accuracy of approximately 99.68%. With that being said, the conclusions that can be drawn here are that the DNN is primarily data specific and does not transfer over to a different environment or object, i.e., the LED type very well. However, the model architecture with its parameters is capable of being trained on a new dataset very well and is able to perform very well on the new specific data. As this was just to bridge a new area in camera-based VLC systems that incorporate DNNs, a lot more research studies and analyses are required to draw definite conclusions, but the main purpose was to expose the reader to another application of DNNs in a camera based VLC system. Figure 9: MIMO Model validated against the underwater LED dataset.
## 4 Performance Probability Analysis
### Connection Probability
Then, the Fredholm determinant of kernek \(\mathcal{K}_{i}\) can be expressed as  
\[Det(\textbf{I}+\beta\mathcal{K}_{i})=\prod_{n\geq 0}(1+\beta\zeta_{n}). \tag{18}\]  
Therefore, (15) can be described as  
\[Det(\textbf{I}+\beta\mathcal{K}_{i})^{-\frac{1}{\beta}}=\prod_{n\geq 0}(1+\beta \zeta_{n})^{-\frac{1}{\beta}}. \tag{19}\]  
For simplicity, we introduced a closed form expression for the Fredholm determinant. According to [PERSON]'s theorem, the kernel \(\mathcal{K}_{i}(\textbf{x},\textbf{y})\) can be written as:  
\[\mathcal{K}_{i}(\textbf{x},\textbf{y})=\Sigma_{n\geq 0}\zeta_{n}\phi_{n}(\textbf{ x})\overline{\phi_{n}(\textbf{y})}, \tag{20}\]where \(\zeta_{n}\) and \(\phi_{n}(\mathbf{x})\) are the basis of eigenvectors and corresponding eigenvalues of \(\mathcal{K}_{i}(\mathbf{x},\mathbf{y})\), respectively. For \(\mathbf{x}\in\mathbb{R}^{2}\) and \(n\geq 0\), we can know that \(\phi_{n}\) is an orthogonal basis and \(\zeta_{n}\) is eigenvalues. There are \(\int_{\mathbb{R}^{2}}\mid\phi_{n}(\mathbf{x})\mid^{2}d\mathbf{x}=1\) and \(\int_{\mathbb{R}^{2}}\phi_{n}(\mathbf{x})\overline{\phi_{n}(\mathbf{x})}d \mathbf{x}=0\). Define  
\[\zeta_{n} =\frac{2(\pi\rho)^{n+1}}{n!}\int_{0}^{L}\eth(x)^{2}\exp(-\pi\rho x ^{2})x^{2n+1}dx \tag{21}\] \[\phi_{n}(\mathbf{x}) =\sqrt{\frac{\rho}{v_{n}n!}}\eth(|\mathbf{x}|)(\sqrt{\pi\rho} \mathbf{x})^{n}\exp\Big{(}\frac{\pi\rho}{2}|\mathbf{x}|^{2}\Big{)}.\]  
Therefore, \(Det(\mathbf{I}+\beta\mathcal{K}_{i})^{-\frac{1}{\beta}}\) can be represented as:  
\[Det(\mathbf{I}+\beta\mathcal{K}_{i})^{-\frac{1}{\beta}}= \tag{22}\] \[=\prod_{n\geq 0}\bigg{(}1+\frac{2\beta(\pi\rho)^{n+1}}{n!}\int_{0 }^{L}\exp(-\pi\rho x^{2})x^{2n+1}\eth(x)^{2}dx\bigg{)}.\]  
Thus, the connection probability can be rewritten as  
\[\mathbf{P}_{con}= \prod_{i=1}^{N}\exp(-A_{\mathrm{Y}}T_{0})\times \tag{23}\] \[\prod_{n\geq 0}\bigg{(}1+\frac{2\beta(\pi\rho)^{n+1}}{n!}\int_{0 }^{L}\exp(-\pi\rho x^{2})x^{2n+1}\eth(x)^{2}dx\bigg{)}.\]  
It can be seen that the connection probability is influenced by the number of relay nodes, the repulsive parameter, and the beam width. According to (23), the relevant parameters can be adjusted to maintain a high connection probability, ensure the stability of the information link, and improve the reliability of data transmission.
# Metaheuristics and Support Vector Data Description for Fault Detection in Industrial Processes
###### Abstract  
In this study, a system for faults detection using a combination of Support Vector Data Description (SVDD) with metaheuristic algorithms is presented. The presented approach is applied to a real industrial process where the set of measured faults is scarce. The original contribution in this work is the industrial context of application and the comparison of swarm intelligence algorithms to optimize the SVDD hyper-parameters. Four recent metaheuristics are compared hereby to solve the corresponding optimization problem in an efficient manner. These optimization techniques are then implemented for fault detection in a multivariate industrial process with non-balanced data. The obtained numerical results seem to be promising when the considered optimization techniques are combined with SVDD. In particular, the Spotted Hyena algorithm outperforms other metaheuristics reaching values of F1 score near 100% in fault detection.  
Keywords:support vector data description; metaheuristics; fault detection; one class classification +
Footnote †: journal: Applied Sciences
# Analysis of BDS-3 Onboard Clocks Based on GFZ Precise Clock Products
## 2 Methods and Data Collection  
In this section, the precision clock products used and the detailed information of BDS-2/3 satellites are reviewed. Then, the preprocessing method for outlier detection is introduced. Finally, the satellite clock offset model of evaluating the performance of onboard satellite clocks in this paper is presented.
# Research and Application of Foam Filling Material in Soft Rock Roadways
## 4 Foam Filling Material Performance Index Test
### Mechanical Strength Test of Materials  
It is only with effective resistance to failure that filling materials can play a protective role after being applied, ensuring the normal progress of filling tasks and achieving the expected filling effect. According to SL237-1999 Geotechnical Test Regulations, the well-matched grout was injected into a cuboid model with a side length of 7 cm, removed after 24 h, and then placed under specific environmental conditions for 7 days of maintenance. The environmental conditions were set at a room temperature of 18-22 \({}^{\circ}\)C and a relative humidity of 45-55%. After 7 days of maintenance, the RMT-150B electro-hydraulic servo testing machine was used to test the samples. Each group was tested three times, and the average value was used as the test result.  
The test results are shown in Table 4. According to the findings, for the test cube, the sample with a side length of 70.7 mm exhibited a certain mechanical strength when removed from the model after 1 day, showing a 100% increase compared to the strength measured 7 h after removal. After 7 days and beyond, the strength increased steadily. For foaming test cubes with water-solid ratios of 2:1 and 2.5:1, the final strength was not significantly enhanced compared to the strength measured 1 day after removal. In contrast, for foaming materials with water-solid ratios of 1.5:1 and 1:1, the final strength increased by approximately 47.7% compared to the strength measured one day after removal.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Water–Solid Ratio**} & \multirow{2}{*}{**Foam Content/wt\%(C)**} & \multicolumn{3}{c}{**Uniaxial Compressive Strength/MPa**} \\ \cline{3-5}  & & **1 d** & **7 d** & **28 d** \\ \hline \multirow{5}{*}{1:1} & 1.5 & 3.28 & 4.28 & 4.98 \\  & 2 & 2.77 & 4.11 & 4.81 \\  & 2.5 & 2.46 & 4.04 & 4.78 \\  & 3 & 1.92 & 3.92 & 4.56 \\  & 3.5 & 1.78 & 3.72 & 4.49 \\ \hline \multirow{5}{*}{1.5:1} & 1.5 & 1.88 & 2.98 & 3.89 \\  & 2 & 1.78 & 2.72 & 3.42 \\  & 2.5 & 1.63 & 2.36 & 3.19 \\  & 3 & 1.59 & 2.38 & 2.99 \\  & 3.5 & 1.52 & 2.21 & 2.84 \\ \hline \multirow{5}{*}{2:1} & 1.5 & 1.56 & 1.83 & 2.09 \\  & 2 & 1.37 & 1.78 & 1.99 \\  & 2.5 & 1.25 & 1.36 & 1.86 \\  & 3 & 1.19 & 1.27 & 1.76 \\  & 3.5 & 1.09 & 1.19 & 1.49 \\ \hline \multirow{5}{*}{2.5:1} & 1.5 & 1.29 & 1.59 & 1.89 \\  & 2 & 1.18 & 1.49 & 1.62 \\ \cline{1-1}  & 2.5 & 1.09 & 1.36 & 1.44 \\ \cline{1-1}  & 3 & 0.98 & 1.29 & 1.23 \\ \cline{1-1}  & 3.5 & 0.87 & 1.14 & 1.02 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Uniaxial compressive strength test table.
## 2 Review of the [PERSON]'s Protocol
### Authentication Phase  
In this phase, the \(N\) establishes a session key with the _HN_ through the _FN_ as follows. The whole process is given in Figure 3.  
Figure 3: [PERSON]’s protocol.  
1. A second level node \(N\) selects a random number \(r_{N}\) and computes \[x_{N}=a_{N}\oplus id_{N},\] (1) \[y_{N}=x_{N}\oplus r_{N},\] (2) \[tid_{N}=h(id_{N}\oplus t_{N},r_{N}),\] (3) where \(t_{N}\) is the current timestamp. Next, the \(N\) sends \(\langle tid_{N},y_{N},a_{N},b_{N},t_{N}\rangle\) to the \(FN\).
2. After receiving the message from the \(N\), the \(FN\) places his identity, \(id^{\prime}_{N}\), in the message and forwards the message \(\langle id^{\prime}_{N},tid_{N},y_{N},a_{N},b_{N},t_{N}\rangle\) to the \(HN\).
3. Once receiving messages from the \(FN\), the \(HN\) first checks the \(id^{\prime}_{N}\) in its database. The process will be terminated if fails. Then, the \(HN\) checks the timestamp \(t_{N}\) by judging \(t^{*}-t_{N}\stackrel{{?}}{{<}}\delta t\), where \(t^{*}\) is the time when the message is received, with \(\delta t\) being the maximum transmission delay. Next, the \(HN\) computes the following: \[k^{*}_{N}=k_{HN}\oplus a_{N}\oplus b_{N},\] (4) \[x^{*}_{N}=h(k_{HN},k^{*}_{N}),\] (5) \[id^{*}_{N}=x^{*}_{N}\oplus a_{N},r^{*}_{N}=x^{*}_{N}\oplus y_{N},\] (6) \[tid^{*}_{N}=h(id^{*}_{N}\oplus t_{N},r^{*}_{N}),\] (7)
4. which checks whether \(tid^{*}_{N}\stackrel{{?}}{{=}}tid_{N}\). If the equation holds, the \(HN\) ensures that the \(N\) is legal. The \(HN\) picks temporary secret parameters \(f_{N},k^{+}_{N}\) and continues to compute the following: \[\alpha=x^{*}_{N}\oplus f_{N},\] (8) \[\gamma=r^{*}_{N}\oplus f_{N},\] (9) \[a^{+}_{N}=id^{*}_{N}\oplus h(k_{HN},k^{+}_{N}),\] (10) \[b^{+}_{N}=k_{HN}\oplus a^{+}_{N}\oplus k^{+}_{N},\] (11) \[\eta=\gamma\oplus a^{+}_{N},\] (12) \[\mu=\gamma\oplus b^{+}_{N},\] (13) \[\beta=h(x^{*}_{N},r^{*}_{N},f_{N},\eta,\mu).\] (14)
5. Finally, the \(HN\) stores the session key \(k_{s}=h(id^{*}_{N},r^{*}_{N},f_{N},x^{*}_{N})\) and sends the message \(\langle\alpha,\beta,\eta,\mu,id^{\prime}_{N}\rangle\) to the \(FN\).
6. Once the \(FN\) receives the message from the \(HN\), it drops his identity \(id^{\prime}_{N}\) and sends the message \(\langle\alpha,\beta,\eta,\mu\rangle\) to the \(N\).
7. Now, the \(N\) computes \(f^{*}_{N}=x_{N}\oplus\alpha,\beta^{*}=h(x_{N},r_{N},f^{*}_{N},\eta,\mu)\) and checks \(\beta^{*}\stackrel{{?}}{{=}}\beta\) to determine whether the \(HN\) is legal or not. The authentication process will terminate if the equation does not hold. Then, the \(N\) computes \(\gamma=r_{N}\oplus f^{*}_{N},a^{+}_{N}=\gamma\oplus\eta\), and \(b^{+}_{N}=\gamma\oplus\mu\). Afterwards, the \(N\) stores the session key \(k^{*}_{s}=h(id_{N},r_{N},f^{*}_{N},x_{N})\) and replaces the parameters \((a_{N},b_{N})\) with the parameters \((a^{+}_{N},b^{+}_{N})\).
## 2 Transition Modeling
### Separation-Induced Transition Correlation  
It is known that when a laminar boundary layer separation occurs, the present transition model consistently predicts the TR location too far downstream [11]. This is because the turbulent kinetic energy (\(k\)) in the separating shear layer is smaller at lower free-stream turbulence intensities. Therefore, a modification for the separation-induced transition is applied by the following equation:  
\[\gamma_{sep}\ =\ min\left(C_{s1}max\left[\left(\frac{Re_{v}}{3.235 Re_{\theta c}} \right)\ -\ 1,0\right]F_{reattch},2\right)F_{\theta t} \tag{11}\]  
\[F_{reattch}\ =\ e^{-\left(\frac{\beta T_{s}}{20}\right)^{4}} \tag{12}\]  
\[\left(\gamma_{eff}\ =\ \max\left(\gamma,\gamma_{sep}\right)\right) \tag{13}\]  
where, \(C_{s1}\) is a constant with a value of 2. The model constants in this equation have been adjusted from those by [PERSON] et al. [11] in order to improve the predictions of the separated flow transition. The main difference is that the constant that controls the relation between \(Re_{v}\) and \(Re_{\theta c}\) was changed from 2.193 for a Blasius boundary layer to 3.235 at a separation point where the shape factor is 3.5 [11]. The transition model has been coupled with [PERSON]'s \(k\)-\(\omega\) SST turbulence model [15], in which the production and destruction terms from the turbulent kinetic energy equation in the original SST model have been modified using the intermittency. In order to capture the laminar and transitional boundary layers correctly, the mesh should have a \(y^{+}\) of approximately one.
## 2 Methodology
### Optimization Procedure
#### 2.1.3 Multiple-Attribute Decision-Making-TOPSIS  
The output result of a multi-objective optimization model is a Pareto non-dominated solution set composed of a series of optimal solutions; that is, each solution is undifferentiated, making it difficult to judge the advantages and disadvantages of different solutions for the four optimization objectives. A multi-attribute decision-making method is needed to determine the best scheme from a series of design schemes. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is one of the multi-objective decision-making methods that is used for ranking the alternatives to the decision-making problem; it is based on choosing the alternative that has the closet distance to the ideal positive solution and the longest distance from the ideal negative solution. The ideal positive solution is based on maximizing the beneficial criteria, while the ideal negative solution is based on minimizing the non-beneficial criteria [38; 39]. Firstly, the data of the Pareto non-dominated solution set are normalized. Secondly, the weighting method is applied to assign weight coefficients of 0\(\sim\)1 to the four optimization objectives, of which the sum is 1. The weight coefficient reflects the preference of decision-makers, and different decision-makers can change the weight value according to their different needs for the four objectives. Thirdly, the TOPSIS method is used to evaluate the solution set comprehensively in order to judge the closeness between the current optimal solution and the ideal solution. Finally, the optimal scheme is selected according to the degree of proximity. The calculation formulae are as follows:  
\[C_{i}=\frac{D_{i}}{D_{i}^{+}+D_{i}^{-}} \tag{1}\]  
\[D_{i}^{+}=\sqrt{\sum_{j=1}^{\overline{m}}\omega_{j}\left(Z_{j}^{+}-z_{ij} \right)^{2}} \tag{2}\]  
\[D_{i}^{-}=\sqrt{\sum_{j=1}^{\overline{m}}\omega_{j}\left(Z_{j}^{-}-z_{ij} \right)^{2}} \tag{3}\]  
where \(C_{i}\) is the final score of each evaluation solution, \(0\leq C_{i}\leq 1\); when \(C_{i}\) is closer to 1, the evaluation solution is better. \(D_{i}^{+}\) and \(D_{i}^{-}\) are the distances between the \(i\)th evaluation object and the optimal scheme or the worst scheme, respectively; \(z_{ij}\) is the normalized weighted matrix; \(Z_{j}^{+}\) and \(Z_{j}^{-}\) are the optimal scheme and the worst scheme, respectively, which are composed of the maximum and minimum values of each column element in the matrix; and \(\omega_{j}\) is the weight of the \(j\)th evaluation index.
## 3 Building Load Type
### Classify Based on Building Type
#### 3.2.5 Residential Buildings  
With the increase in population and the robust development of the economy, residential electricity consumption has risen significantly, making mid-to-short-term forecasting of the total load demand in residential communities crucial [100]. Such forecasting not only helps balance power supply and demand but also provides strong support for implementing precise demand response strategies [101]. Short-term residential load forecasting serves as a vital foundation for constructing efficient energy distribution strategies [102]. However, due to the diversity and randomness of residents' electrical appliance usage, constructing accurate prediction models at the individual household level is indeed a challenge [103]. As shown in Figure 10, an example of load demand forecasting for residential buildings is presented.  
Figure 9: A schematic diagram of the load forecasting process for commercial buildings [99].  
Residential buildings' load types mainly focus on electricity and heat [105; 106]. Nowadays, with the emergence of vast residential energy consumption data, research directions in data-driven technologies have been further expanded [107]. Traditional machine learning methods struggle to model the unbalanced energy consumption patterns of residents, whereas the improvement in data foundations has made deep learning models an effective data-driven technology [108]. To enhance the accuracy of these prediction methods, it is not only necessary to properly handle noise and uncertainties in the data sources but also to optimize the parameters of the prediction models using optimization methods. This will be the key to improving prediction accuracy [109].
## 4 Discussion
Therefore, the total radiation activity during brachytherapy treatment may be an indicator for estimating a physician's eye dose. The hand dose was \(\sim\)0.5 mSv/procedure in this study, which is roughly equivalent to other results for doses received by brachytherapy physicians, such as when using \({}^{125}\)I seeds for prostate cancer [71; 72]. According to our assessment, \({}^{198}\)Au brachytherapy physicians are unlikely to exceed the regulatory hand dose limit (500 mSv/y), as it is unlikely that a physician will perform more than 1000 procedures annually. We found a higher dose in the right hand than in the left hand; as the physician was right-handed, this was probably because the right hand was nearer the radiation source for longer than the left hand. The physician manually loaded the radiation sources in this study. To reduce the physician dose, manufacturers need to develop a more convenient, automatic system for loading the sources into the treatment needles. During the implantation of the grains into the tongue, a brachytherapy physician cannot use additional lead shielding equipment because it would impede the work (Figure 4). To reduce the eye dose, we recommend that the brachytherapy physician wear leaded eyeglasses during implantation, if available. Even if the clinical advantages of \({}^{198}\)Au permanent grain brachytherapy for treating tongue cancer are evident, the assessment of the physician's eye radiation dose during \({}^{198}\)Au grain brachytherapy is important, particularly in consideration of the significant reduction in the occupational eye dose limit from 150 to 20 mSv/year. We evaluated and estimated eye doses for \({}^{198}\)Au grain brachytherapy physicians using an eye dosimeter and a personal neck badge, and measured the hand dose received by the physician. The results demonstrated that the eye dose for physicians during \({}^{198}\)Au brachytherapy warrants attention in the absence of complete radiation protection. An additional Pb shielding device is needed for radiation protection during \({}^{198}\)Au brachytherapy. To date, no studies have investigated physicians' eye doses in \({}^{198}\)Au brachytherapy. Despite the limited scope of this study, the results contribute valuable insights into radiation safety for \({}^{198}\)Au brachytherapy physicians. As this was a single-institutional study, further research, such as a multi-institutional studies involving a larger number of cases, will be necessary.
## 4 Experimental Setup
### Ultrasound Lock-in Thermography  
For the ULT, the same piezoelectric transducer as for the SLDV measurements is used to excite the specimen with linear sine chirp signals with a length of \(\tau=5\,\mathrm{s}\). To excite the defect fasteners in the riveted aluminium plate, measurements with different frequencies between 3 kHz to 150 kHz have been performed. The lowest frequency is set to 3 kHz because of the small excitation time of 5 s which is kept constant for all measurements. Hence, low frequencies will only allow a very limited number of periods in an excitation time window. The higher frequency of 130 kHz underlies the used electronics and stimulating piezoelectric water which does not allow a sufficient excitation at higher frequencies. To enhance the signal-to-noise ratio (SNR) despite the high thermal conductivity and heat capacity of the specimen (AL2020-T3), the piezoelectric water is driven with an input voltage of 100 V which is provided by a piezoelectric driver (_FLC Electronics AB FA20_). To increase the emissivity of the specimen and capture the low temperature increase at the surface, some chalk spray (KD Check SD-1), which is normally used as the developer in dye penetrant testing, is applied to the surface. This spray is approved to DIN EN ISO 3452-2 and is therefore suitable for the use in an industrial environment.  
For the measurement of the surface temperature, the same thermal camera as for the IPHWT which is described above is used. The captured videos are processed with an in-house written plugin for the open-source image processing software platform ImageJ, which calculates the phase and amplitude response of each pixel to the periodic excitation of the specimen based on the thermal video and the chirp length of the excitation signal. In this work, no synchronization between the video capturing and the piezoelectric transducer excitation was necessary as it was not intended to receive accurate phase information but to grasp the general periodic thermal response of the specimen to the excitation signal.