esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Numerical Validation  
In this subsection, we validate the correctness of \(w\) derived in Equations (22)-(24). The numerical simulations use the following mechanical and geometric properties [31]:  
\[\begin{split} E&=210(\text{GPa}),\quad m=60.3665( \text{kg}),\quad I=30.55\times 10^{-6}(\text{m}^{4}),\\ k&=35.03(\text{MN}/\text{m}^{2}),\quad P=10(\text{kN}). \end{split} \tag{25}\]  
The velocity \(v\) of the moving load and the damping coefficient \(c\) are treated as key variables in the analysis. From Equation (19), the curves for \(g_{j_{0}}(c,v)=0\) are plotted in Figure 2a, showing critical velocity \(v_{m}\) and damping coefficient \(c_{m}\). Three points, \(\psi_{1,2,3}\), are marked in Figure 2a with the coordinates \(\psi_{1}(1.7325\times 10^{6},10)\), \(\psi_{2}(1.7325\times 10^{6},50)\), and \(\psi_{3}(9\times 10^{6},10)\). Solving the first equation in Equation (8) for these points yields  
\[\xi=\left\{\begin{array}{ll}\pm 1.3500+1.0990i,\pm 0.7707-1.0990i,&\quad for \quad point\ \psi_{1},\\ \pm 2.0756+1.3152i,-0.4067i,-2.2236i,&\quad for\quad point\ \psi_{2},\\ \pm 2.0988+1.3278i,-0.3909i,-2.2647i,&\quad for\quad point\ \psi_{3}.\end{array}\right. \tag{26}\]  
From Equations (23) and (24), we have  
\[W_{b}=\left\{\begin{array}{ll}10^{-4}\times[1.4216\cos{(0.7707y)}-2.5427\sin {(0.7707y)}]e^{1.0990y},&\quad for\quad point\ \psi_{1},\\ 10^{-4}\times[1.1796e^{h.4067y}-0.5097e^{2.2236y}],&\quad for\quad point\ \psi_{2},\\ 10^{-4}\times[1.1304e^{h.3909y}-0.4805e^{2.2647y}],&\quad for\quad point\ \psi_{3}.\end{array}\right. \tag{27}\]  
\[W_{f}=\left\{\begin{array}{ll}10^{-4}\times[1.4216\cos{(1.3500y)}+0.863\sin{( 1.3500y)}]e^{-1.0990y},&\quad for\quad point\ \psi_{1},\\ 10^{-5}\times[6.6987\cos{(2.0756y)}+1.0952\sin{(2.0756y)}]e^{-1.3152y}],&\quad for \quad point\ \psi_{2},\\ 10^{-5}\times[6.4989\cos{(2.0988y)}+1.0317\sin{(2.0988y)}]e^{-1.3278y}],&\quad for \quad point\ \psi_{3}.\end{array}\right. \tag{28}\]  
A comparison between the analytical solutions from Equations (27) and (28) and the numerical integration of the inverse Fourier transform of Equation (7) for the points \(\psi_{1,2,3}\) are shown in Figure 2b, c, and d, respectively. It is evident from Figure 2 that the analytical results perfectly match the numerical results.  
To mimic the characteristics of actual sensor measurements, the displacement data from Equations (27) and (28) are intentionally perturbed with noise. Additionally, the analytical solution is used to generate response data for a wide range of parameter values, serving as reference benchmarks. In this study, the governing Equation (5) is not normalized. Instead, displacement data derived from the analytical solutions (27) and (28) are standardized, resulting in a dataset with a mean of 0 and a standard deviation of 1. Both the displacement data used in the PINN model's loss function and the model's predicted outputs are standardized values.
# Design of an Optically Transparent Microwave Absorber Based on Coding Metasurface
## 5 Conclusions  
In this paper, a metamaterial absorber with a checkerboard patterned ITO film as the surface is obtained by using flexible and optically transparent wave-absorbing material (ITO-PET) and a coding arrangement of two basic coding units based on the APS-PSO algorithm. The experimental results, which are consistent with the simulation results, show that the absorber has superior performance: over 90% absorptance in the 5.06-9.01 GHz band, high transmittance, and a \(-\)10 dBm RCS reduction in the 5.3-8.7 GHz band. This design also has polarization insensitivity and angular stability, with promising applications in flat panel displays, low radiation glass, special functional window coatings, etc.  
Conceptualization, [PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] made the samples, [PERSON], [PERSON] and [PERSON] performed the experiments; [PERSON], [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON] technical construction for coatings; [PERSON], and [PERSON] wrote the paper. All authors critically reviewed the content and approved final version for publication. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Yong Innovative Talents Project, Guangdong Province (2019 KQNCX069), in part by Special Fund for Science and Technology Innovation Strategy of Guangdong Province (Climbing plan, pdig2020b0344), and in part by Basic and Applied research of Guangzhou Basic Research Plan (202102020307), and in part by the open funding of Guangdong Provincial Key Laboratory of Millimeter-Wave and Terahertz under Grant 2019B030301002 KF2105.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the corresponding author upon reasonable request.  
**Acknowledgments:** We would like to thank [PERSON] and [PERSON] from Nanjing University of Science and Technology for their help with the testing of the experiment.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 13: The comparison of the characteristics of previously reported and proposed optically transparent microwave absorbers [27; 28; 29].
## 7 Discussion of the Filtering Impact on Security and Privacy
### Influence of Filtering on STRIDE Attack Vectors
_S\({}_{\beta}\) (Local Network Communication):_  
According to the definition of scenario _S\({}_{\beta}\)_ in Section 5, the _Smart Home Devices_ in the local network are not obligated to communicate with each other, which means that no rules exist that permit any traffic other than that which is originating from the _Ouner Control Device_ to the _Smart Home Device_\(X\). Because the former is explicitly not considered as an attack device, the influence levels of the local attack vectors _DNL_, _ENL_, _INL_, _MML_, _MNL_, _RNL_ and _XNL_ are increased to _high_ for traffic from or to _Smart Home Device_\(X\). _S\({}_{\gamma}\) (Guest Access):_  
Similar to _S\({}_{\beta}\)_, there is no need for communication between the two _Smart Home Devices_ according to the specification of Scenario _S\({}_{\gamma}\)_ (see Section 5). Because the _Accessible Smart Home Device_ is intended to be accessed by the _Guest Control Device_, there is a need for rules to be in place which allow communication between the two appliances. On the other hand, the _Private Smart Home Device_ should by definition not be accessed by the guest, and therefore no rules permitting any communication between them would be in place in _Bark_. Overall, it is assumed that out of the entities in the local network, _Bark_ would only allow the _Owner Control Device_ to communicate with the _Private Smart Home Device_. Because the owner has been explicitly excluded as a threat origin, the influence levels of the local attack vectors _DNL_, _ENL_, _INL_, _MML_, _MNL_, _RNL_ and _XNL_ are increased to _high_ for traffic from or to the _Private Smart Home Device_. \(S_{\delta}\) _(Indirect Control):_  
Following the assumptions for this scenario (Section 5), the _Smart Home Device_ can only be controlled indirectly. Thus, there is no need for local communication between this device and the _Smart Hub_. Consequently, there would not be any need for filtering (e.g., no need to whitelist local communication), and hence all attack vectors which are solely based on local communication (i.e., _RNL_ and _DNL_, _RNL_ and _ENL_, _RNL_ and _INL_, _RNL_ and _MML_, _RNL_ and _RNL_, and _RNL_ and _XNL_) are influenced at a _high_ level. Moreover, the influence on the _DOS_ vector is increased to _medium_ due to the absence of internal attacks and, in contrast to the previous scenarios, no reflections using the _Owner Control Device_ are possible. However, the possibility of an external attack remains.
## 3 Results and Discussions
### Results of Strength Tests  
Results of concrete strength tests are shown in Table 1. It can be found that the compressive strength of the uncracked samples with minor corrosion and moderate corrosion varied widely from 40.5 to 58.3 MPa and 42.4 to 57.1 MPa, respectively. The cracked samples with severe corrosion varied from 42.5 to 58.0 MPa.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Cover** & **Cover Depth** & **Carbonation** & **Compressive** & **\({\mathbf{i}}_{\mathbf{\mathrm{max}}}\) (mm)** & **Corrosion Status** \\
**Sample** & **(mm)** & **Depth (mm)** & **Strength (MPa)** & **\({\mathbf{i}}_{\mathbf{\mathrm{max}}}\) (mm)** & **of Rebars** \\ \hline A1 & 23.14 & 27.64 & 55.3 & 0.25 & severe corrosion \\ A2 & 23.64 & 16.02 & 54.4 & 0.06 & severe corrosion \\ A3 & 22.
## 4 Results and Discussion
### Sensitivity Analysis of Input Variables
When constructing the LSTM model, in the face of the incompleteness of historical data, the equipment degradation model is introduced to estimate the time-series variation of the failure probability of the BEs, and the failure probability of the TE is calculated by the corresponding calculation  
Figure 11: SHAP analysis of BEs. method of the DFT. To construct the LSTM model dataset, a total of 14,400 datasets are computed and counted, of which 80% is used to construct the training set, and the remaining 20% is used for the test set. In order to verify the applicability and superiority of the proposed model, the corresponding DFT time-series solution model is developed based on the traditional recurrent neural network (RNN) method. The time-series failure probabilities obtained from the Hidden Markov Chain are processed according to the data preprocessing method proposed earlier. The processed time-series dataset is then input into the previously constructed LSTM and RNN models to observe the differences between the output values of the two models and the true values. As shown in Figure 12, it can be seen that the LSTM model has a better performance in capturing the time-series variation. The model training results show that the traditional RNN model has a training loss of 2.1473 \(\times\) 10\({}^{-4}\), whereas the training loss of the constructed LSTM model for storage tank accidents is only 5.0337 \(\times\) 10\({}^{-5}\), as shown in Figure 13. This significant improvement confirms the superiority of the LSTM model in dealing with DFT time-series prediction. The lower training loss achieved by the LSTM model is attributed to its unique gating mechanism, which allows the model to capture the long-term dependencies in the time series more efficiently while avoiding the gradient vanishing or explosion problems that are commonly found in the traditional RNN models. In addition, the LSTM model for storage tank accidents not only performs well during the training process but also shows great accuracy and reliability in predicting the future trend of risk probability evolution and is able to more accurately predict the change in the probability of failure of storage tank leakage accidents. Considering the model training loss, prediction accuracy, and model complexity together, this LSTM model proves its potential as an effective probabilistic analysis tool. It provides a new analytical approach to hazardous chemical storage tank safety management and can play an important role in risk assessment and preventive measure development. Figure 12: Comparison of RNN and LSTM prediction results.
# A Novel Framework for Online Remaining Useful Life Prediction of an Industrial Slurry Pump
## 3 Results  
The _RUL_ prediction results obtained by the developed model for channel 2 and channel 4 HDTs are shown in Figures 8 and 9, respectively. It is obvious that when the number of operating hours was less for the model's learning/training stage, the predicted _RULs_ were less accurate. However, with the passage of time, when the number of operating hours was more, the _RUL_ prediction results were relatively more accurate. It should be noticed that each data point of an HDT was reflecting one operating hour. Therefore, _RUL_ prediction results were calculated by dividing the x-coordinates of the prediction point by 24 (which was meeting the threshold line). The _RUL_ prediction results obtained by the developed model were also compared with other existing methods. These comparative methods are typically used for online _RUL_ prediction in the absence of run-to-failure data, as discussed in [13; 17; 20]. The obtained _RUL_ prediction results comparison is shown in Tables 3 and 4. It should be noticed that the curve-fitting method was not applicable to predict the _RUL_ for all the selected operating hours of channel 2. The reason is that the predicted curve was striking to the threshold line after the threshold (maximum) point of the HDT, as shown in Figure 8a-f. Contrariwise, the Nonlinear Autoregressive network with Exogenous input (NARX) method was predicting its _RUL_ prediction results for only 456 operating hours, as shown in Table 3. Similarly, in the case of channel 4, it can be observed in Figure 9 that the proposed method _RUL_ prediction results are more satisfactory than the curve-fitting method results. The proposed model results are better for four out of seven operating hours, i.e., 655, 720, 824, and 834 operating hours, as displayed in Figure 9d-g. For the remaining three operating hours, i.e., 456, 496, and 565, the curve-fitting method results were slightly better, as depicted in Figure 9a-c. In contrast, the NARX method provided _RUL_ prediction results for only 824 and 834 operating hours, but those results were less accurate than the developed model results, as presented in Table 4.  
Figure 8: Online _RUL_ prediction results obtained by the curve-fitting and proposed methods for channel 2, (**a–f**).  
Figure 9: Online _RUL_ prediction results, obtained by the curve-fitting and proposed methods for channel 4, (a–g).
# An Overview of Fog Data Analytics for IoT Applications
## 4 Current State-of-the-Art
### Healthcare  
The healthcare industry is going through a total paradigm shift from cloud-based applications to fog-based IoT device data management. This transition is due to its requirement of a high availability with geospatial awareness and an instant response to events reported by health-monitoring sensors. Body area networks (BANs) are most popular for monitoring the health of individuals without any human observer in a friendly way. They also provide notifications to relatives and instantly contact emergency services with complete automation. Fog computing power requirements of healthcare have severe time constraints. Hospitals deploy fog nodes for collecting data from nearby IoT devices and accommodate the need to monitor the health condition of many patients. Fog enables smart healthcare, which notifies appropriate staff members when patients are in critical condition with their geospatial location. Various methods have used deep learning to detect epileptic seizures [43], heart disease [44], and cancer classification [45] in combination with fog nodes and data from IoT devices on the fly. Systems such as fog ambient assisted living (FAAL) [46] have been developed to help people suffering from neurological disorders, and personalized healthcare support for remote patients with diabetes is making a tremendous impact on people's lives [47].
## 4 Normalized Power to the Volume
Even if the tip mass is made out of a material of a density in the range of the average density of the beam, the advantage of an increase in the tip mass is not significant. Hence, in order to optimize the PEHs under a constant volume, the tip mass must be made out of highly dense materials. Figure 7 displays the normalized power to the total occupied volume \(\mathrm{NPV_{Total}}\) (of the beam, the tip mass and the motion). The density of the tip mass is assumed to be 20 g/cm\({}^{3}\) as the densest materials currently are in this range. At a very low input excitation level (\(u=0.8~{}\upmu\)m), the \(\mathrm{NPV_{Total}}\) at the corresponding \(R_{op}\) and \(\omega_{n}\) is larger than that at the corresponding \(R_{d}\) and \(\omega_{d}\), while at a relatively larger input excitation level (\(u=180~{}\upmu\)m), the \(\mathrm{NPV_{Total}}\) at the corresponding \(R_{d}\) and \(\omega_{d}\) is larger than that at the corresponding \(R_{op}\) and \(\omega_{n}\). Although the generator at \(R_{op}\) and \(\omega_{n}\) supplies higher power output than at \(R_{d}\) and \(\omega_{d}\), it does not necessarily mean the maximum generated power from a constant predefined volume if the volume of motion is taken into account. The minimum tip displacement, as investigated before, occurs at an applied load resistance of \(R_{d}\) and an excitation frequency of \(\omega_{d}\). In all investigations, the normalized power (\(\mathrm{NPV_{b}}\), \(\mathrm{NPV_{b+mt}}\), or \(\mathrm{NPV_{Total}}\)) is constant at variant widths if other geometrical parameters (the free length, the total thickness, and the mass ratio) are kept constant. Figure 6: Normalized power to the volume of the beam and the tip mass (\(\mathrm{NPV_{b+mt}}\)) at **(a)** the corresponding \(R_{op,res}\) and \(\omega_{n}\) **(b)** the corresponding \(R_{d}\) and \(\omega_{d}\) (\(f_{n}\) constant at 155 Hz, free length is 9.62 cm, mass ratio \(\beta\) varies from 0 to 0.52 for the corresponding total thickness varies from 0.382 to 0.682 cm, respectively, \(\zeta_{m}=0.01\), and \(u=0.8~{}\upmu\)m).
## 5 Results and Discussion
### Ring Shear Results  
#### 5.3.1 Effect of Contamination on Shear Strength  
The effect of atrazine contents on peak shear strength (vp) of the AC, BC and KC specimens under 200 kPa normal stress were shown in Figure 8a. As can be seen, increasing the atrazine content of the specimens, reduced the peak shear strength of the specimens. For instance, the peak shear strength of the uncontaminated AC specimen was 0.279 MPa while this value reduced to 0.247, 0.2 and 0.15 MPa after addition of 2, 4, and 6% atrazine to the specimens. This trend was identical for BC and KC specimens after the addition of the same atrazine content. While the peak shear strength values of the BC and KC specimens were 0.345 and 0.2 MPa these values reduced to the range 0.16 \(\leq\) vp \(\leq\) 0.34 and 0.12 \(\leq\) vp \(\leq\) 0.20 after the addition of 2% \(\leq\) ATZ \(\leq\) 6%. The residual stress ratio showed a similar trend, as shown in Figure 8b. For instance, while the residual stress ratio for the uncontaminated AC, BC and KC specimens were 1.39, 1.72 and 0.99 respectively, these values decreased with the addition of atrazine content from 2% to 6%. The residual stress ratio for the AC specimens were between 1.24 and 0.76, BC specimens were between 1.70 and 0.80, and KC specimens were between 0.97 and 0.55.  
[PERSON] and [PERSON] [26] indicated that the reduction of shear strength due to contamination might be attributed to the viscosity of pore fluid. They reported that shear strength of the soil reduced  
Figure 8: Ring shear test results for AC, BC and KC specimens under 200 kPa normal stress for (**a**) peak shear stress versus atrazine content; (**b**) Residual stress ratio versus atrazine content.  
Figure 7: Compaction test results for Amber Clay (AC), Black Clay (BC) and Kaolinite Clay (KC) specimens contaminated at different Atrazine contents (**a**) Optimum moisture content (OMC) versus Atrazine content; (**b**) Maximum dry density (MDD) versus Atrazine content.  
with increasing viscosity of pore fluid. It was mentioned that a viscos pore fluid not only fills the voids amongst the clay particles but also behaves like a lubricant agent and caused the particles to slide and lowers the shear strength value recorded. [PERSON] and [PERSON] [PERSON] and [PERSON] (2018) strengthen their discussion based on the ([PERSON] and [PERSON], 2018; 2018) findings. They observed that increasing the temperature decreased the shear strength of the soil and concluded that the dependency of temperature with shear strength shows that viscosity has a vital role in reducing the shear strength of soil. The used atrazine has a viscosity higher than water, and this characteristic can be the main reason for reduced shear strength and residual stress ratio of the soil when the atrazine content of the soil increased. The presented results in this section was also confirmed by previous studies performed by ([PERSON] and [PERSON], 2018; 2018; 2018).
## 2 Background
### Risks of Reducing Crew on Crew and Vessel Safety
#### 2.4.2 Fatigue  
The IMO defines fatigue is a state of feeling weary and tired due to extended physical or mental work, lack of sleep, and/or exposure to harsh environments. It can affect anyone regardless of training, experience, or skill [39]. With decreasing crew sizes, mariners are proportionally expected to do more with less personnel, potentially meaning less time off and less opportunities to rest. Other factors that can increase the effects of fatigue on mariners include night work, interrupted sleep, long shift hours, and rotating work hours. These conditions are all commonplace within the maritime industry.  
Specific work-rest periods for watch standards are detailed in STCW regulations. According to this, the Officer in Charge of a Navigational Watch (OICNW) for seagoing voyages must receive at least 6 h of continuous rest. A period of rest is defined as a time when personnel are not on watch or on duty. OICNWs must also have a minimum of 10 h of rest within a 24 h period, which excludes drills and emergencies onboard. Within 7 consecutive days, an OICNW cannot receive less than 77 h of rest [40]. Even with these regulations in place, this does not guarantee that mariners are receiving enough rest time to counteract fatigue. According to a study completed in 2020, out of the 20 mariners from 16 different nationalities who were interviewed, all reported work/rest violations onboard their ship [41].  
Fatigue continues to be a worldwide problem. A study conducted by the National Union of Marine, Aviation, and Shipping Transport Officers surveyed 563 seafarers and determined that half of those surveyed indicated that a typical work week was over 85 h. Two-thirds of participants agreed that extra manning was necessary to reduce fatigue felt as a result of excessive work hours [42]. A sample study of accident reports from the National Transportation Safety Board (NTSB) from 2001-2012 found that 20% of the 182 reports reviewed identified fatigue as a contributing factor, finding, or probable cause [43]. In a separate study, the UK's Department of Transportation concluded that roughly one third of all groundings occurring between 1994 and 2003 involved an Officer on watch alone on the bridge who was fatigued [44].