esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of Design Parameters in LSTM Model for Predictive Maintenance
## 3 Optimization of Design Parameters for an LSTM-Based Predictive Maintenance Model
### Design of GA for Exploring Optimal Hyperparameters of LSTM
#### 3.3.1 Chromosome Structure and Initial Population  
The first step for designing the GA is to devise the representation scheme of the chromosome. Since the objective is to find a set of hyperparameters maximizing the accuracy of trained LSTM, we define a chromosome structure as a vector with integer-valued encoding, where each gene expresses the value of a hyperparameter for trained LSTM. If there exist m hyperparameters to be considered, the length of the chromosome is set to m and the whole structure of the chromosome can be depicted as in Figure 6, where each gene corresponds to each hyperparameter of trained LSTM. For example, in Figure 6, the number 3 in the second gene can represent the number of layers in LSTM. Meanwhile, the population used in the GA contains \(P\) chromosomes and an initial population is randomly generated within the lower and upper bounds of hyperparameters, which are defined appropriately.
# A Trusted Lightweight Communication Strategy for Flying Named Data Networking
## 3 Trust Establishment and Trust-Based Monitor Selection
### FANET Distributed Monitor Selection and Monitoring Process  
In our proposal, network participants start by jointly monitoring the network from the beginning. Afterwards, the network monitoring task is distributed among peers trusting each other and hence, they can save their energy during the time other nodes are performing monitoring, as shown in Figure 5.  
In order to make the monitoring process fully distributed among honest UAVs, we have extended the hello messages format with two new fields, MD and MST, respectively (see Figure 6).  
After a certain number of interactions, the trust table of every node will be updated. Thus, every node will start trusting a certain number of neighbors. At this stage, different nodes can start distributing the monitoring process among them by sharing their MD along with the MST through the use of the extended hello messages.  
Every node computes its monitoring duration using the number of trusted neighbors and the duration of the links towards them in such a way that the more trusted neighbors a node has, the lower becomes its monitoring time. Equation (10) outlines the monitoring duration computation.  
\[MD(i)=\begin{cases}0,&\text{if }\#Neighbors=0\\ \frac{AVG[R(i\&_),\_,R(i\&_)]}{\left[\frac{\#TraustNeighbors}{\#Neighbors} \times\frac{1}{\#TraustNeighbors+1}\right]},&\text{else}\end{cases} \tag{10}\]  
In this equation, we multiply the average link duration by factor \(\left[\frac{\#TraustNeighbors}{\#Neighbors}\times\frac{1}{\#TraustNeighbors +1}\right]^{-1}\), which represents the ratio of the trusted direct neighbors. Hence, the higher  
Figure 5: Convergence of our trust-based time division distributed monitoring technique.  
Figure 6: Hello messages format.  
is this ratio, the lower is the monitoring duration. Otherwise, when a UAV is isolated, it does not have to stay in promiscuous mode and evaluate an empty neighborhood. Hence, the monitoring duration is set to 0. In addition, when a UAV receives different MD and MST values from its neighbors, it computes its next monitoring starting time using the last starting time and the monitoring duration of its neighbors as follows:  
\[\text{Next}MST(i)=\text{Last}\text{Trusted}\text{Neighbors}(MST+MD) \tag{11}\]  
Once a monitor detects a malicious node, which means that the malicious node trust value goes below a detection threshold, it broadcasts a one-hop negative recommendation that includes the detected node identity. The receivers of this recommendation will autonomously adjust the indirect trust as previously explained.
## 4 Conclusions  
Effective CO2 selective materials prepared from commercially available P[DADMA][CI] were successfully converted in poly(ionic liquid)-based composite materials with improved CO2 sorption properties. Incorporation of [Pyrr14][Tf2N] and Zn[Tf2N]2 considerably altered the ideal separation performances of the composites. The time-lag experiments show that the addition of the IL improves the CO2 diffusivity in the PIL/IL composite. Furthermore, the supplementary [Tf2N]\({}^{-}\) anions and Zn2\({}^{+}\) cations, supposedly interacting with CO2 molecules, enhance the sorption capacity of the P9 IL6 Zn9 composite. Although certain deviations between the results from direct and indirect solubility, diffusivity and permeability data are identified in the gravimetric and the time-lag experimental approaches, the results obtained capture the dominating contribution of CO2 sorption, and this means that the samples exhibit a predominantly solubility-controlled transport mechanism. PIL/IL/Zn2\({}^{+}\)-based TFC membranes remain functional in a broad range of process conditions investigated, imitating realistic flue gas CO2 capture. Importantly, the improved mixed gas separation performance of composite materials in the humidified conditions demonstrates a combination of transport mechanisms: solution-diffusion and facilitated transport.  
Figure 9: Influence of the temperature on the separation performance of PIL/IL/Zn2\({}^{+}\) composite-based TFC membrane at 26 \({}^{\circ}\)C, 35 \({}^{\circ}\)C and 55 \({}^{\circ}\)C and 1.2 bar in dry (**a**) and humidified (**b**) conditions of the sweep test set-up. The relative humidity of feed gas was ca. 90 % for humidified conditions.  
**Author Contributions:** Conceptualization, D.N.; methodology, D.N.; validation, D.N.; formal analysis, M.T. and A.F.; investigation, D.N., S.L., M.S. and P.L.D., J.J., J.C.J. and M.T.; resources, I.F.J.V., J.C.J. and P.I.D.; data curation, D.N.; writing--original draft preparation, D.N.; writing--review and editing, D.N., M.T., A.F. and J.C.J.; visualization, D.N.; project administration, D.N.; funding acquisition, I.F.J.V. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The financial support of this project by the European Union Seventh Framework Programme FP7/2007-2013 under grant agreement number 608535 is gratefully acknowledged. The authors are also grateful for the financial support from the OT (11/061) funding from KU Leuven, the Belgian Federal Government through I.A.P.- P.A.I. grant (IAP 7/05 FS2), the Polish Ministry of Science and Higher Education through International Co-financed Project grant (2978/7.PR/2014/2) and the Erasmus Mundus fellowship funded by EACEA (EUDIME doctoral programme 4 th edition).  
**Acknowledgments:** Authors acknowledge [PERSON] (SINTEF), [PERSON] (TGA, clean room, SINTEF) and [PERSON] (SEM/SINTEF) for conducting the mentioned measurements and helping with their interpretation.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Analysis of Results
### Analysis of Survey Questionnaire
#### 4.2.2 Objective (b): Investigate How the Mobile/Cloud Devices Are Used Onsite  
The research sought to establish which emerging mobile BIM apps are used onsite. The results are presented in Table 4. It emerged that mobile BIM apps were mostly being used for collaborative purposes in construction projects such as virtual communication between relevant project stakeholders.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Mobile Device** & **Most Often** & **More Often** & **Often** & **Less Often** & **Never** & **Response Count** \\ \hline iPad & 36 & 18 & 24 & 36 & 36 & 150 \\ iPhone & 18 & 24 & 24 & 36 & 48 & 150 \\ Samsung & 12 & 36 & 0 & 24 & 66 & 138 \\ smartphone & 24 & 12 & 6 & 18 & 78 & 138 \\ Samsung note & 6 & 6 & 8 & 42 & 66 & 138 \\ Tablet PC & 0 & 0 & 0 & 6 & 78 & 84 \\ Blackberry & 0 & 0 & 0 & 6 & 120 & 126 \\ Sony & 0 & 0 & 6 & 6 & 114 & 126 \\ HTC & 0 & 0 & 6 & 6 & 114 & 126 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Most common mobile devices.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Mobile/Cloud BIM Application** & **Most Often** & **More Often** & **Often** & **Less Often** & **Never** & **Total Responses** \\ \hline BIM360 & 12 & 12 & 24 & 24 & 84 & 156 \\ BIMx & 6 & 0 & 0 & 12 & 114 & 132 \\ Vico Office Suite & 0 & 0 & 0 & 0 & 132 & 132 \\ Vela Field BIM & 0 & 0 & 0 & 6 & 126 & 132 \\ Bentely Construction & 0 & 0 & 6 & 12 & 114 & 132 \\ Tekla BIM Sight & 0 & 0 & 18 & 12 & 108 & 138 \\ Autodesk Naviswork & 90 & 12 & 24 & 18 & 42 & 186 \\ Solibri model checker & 18 & 36 & 6 & 12 & 72 & 144 \\ BIM360 Glue & 18 & 6 & 12 & 12 & 90 & 138 \\ A360 CAR & 0 & 0 & 0 & 0 & 132 & 132 \\ BIM360 Planning & 0 & 0 & 0 & 0 & 132 & 132 \\ Autodesk A360 & 6 & 12 & 18 & 18 & 90 & 144 \\ BIM Server & 0 & 12 & 0 & 6 & 114 & 132 \\ BIM19 & 0 & 0 & 0 & 0 & 132 & 132 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Cloud BIM software applications.  
Other uses that emerged are the use of mobile BIM apps for teaching and demonstrating projects to onsite project teams, as well as engaging main clients and other stakeholders who have no access or the skills to use the software.
## 4 Self-Selective Routing (SSR)
### Route Repair in Self-Healing Routing (SHR)
\begin{split}&\mathit{AutoSelf-Awareness-Back-offDelay}=\\ &\mathit{Scale}_{\mathit{Factor}}*(\mathit{Hopcount}_{\mathit{ best}}-\mathit{NumHopCount}_{\mathit{expected}}*\mathit{Rand}(0,1)+\mathit{Rand}(0,1))\\ &\mathit{Else\ Hopcount}_{\mathit{best}}\leq\mathit{NumHopCount}_{ \mathit{expected}}\\ &\mathit{AutoSelf-Awareness-Back-offDelay}=\mathit{Global-Awareness-Back-offDelay}\end{split}  \tag{9}\]  
From Equation (8), it is clear that the auto self-awareness back-off delay assigns a delay greater than \(\mathit{Hopcount}_{\mathit{best}}\) to nodes with a hop count greater than \(\mathit{NumHopCount}_{\mathit{expected}}\) parameters. Therefore, the smaller the value of the \(\mathit{Hopcount}_{\mathit{best}}\) parameter, the smaller is the \(\mathit{AutoSelf-Awareness-Back-offDelay}\) parameter and the node has the highest probability of transmitting the packet. The slotted autonomous self-aware back-off slotted formula is as expressed in Equation (10) as [24, 40, 41, 51, 52, 53]:  
\[ \begin{split}&\mathit{AutoSelf-Awareness-Back-offDelay}=\\ &\left\lfloor\frac{\mathsf{AutoSelf-Awareness-Back-offDelay}}{ \mathit{Slot\_Size}}\right\rfloor*\mathit{Slot\_size}\end{split}  \tag{10}\]  
Where the parameters \(\mathit{AutoSelf-Awareness-Back-offDelay}\) are given in Equations (8) and (9), and \(\mathit{Slot\_size}\) is the size of the slot window used. Equation (10) provides the autonomous self-aware and adaptive back-off delay that combines both the continuous and slotted time interval. This approach lowers the back-off delay, and the sensor nodes have the greatest probability of transmitting the packets. In addition, the approach provides the current node with both local and global network state information in order to reduce end-to-end delay for efficient and reliable packet transmission.
# Enhancing Tactile Internet Reliability: AI-Driven Resilience in NG-EPON Networks
## 4 Proposed Architecture
The AI model deployed at the edge can detect anomalies in the network, predicting a variety of faults such as fiber cuts, partial fiber degradation, fiber bending, and faulty splitters. When anomalies are detected, the NEM communicates with the SDN controller to take corrective actions based on the AI predictions, such as activating backup conditions. When faults or degradations occur in the ODN, including fiber cuts, fiber bending, and faulty splitters, the AI model identifies these anomalies and initiates a backup-mode plan. The OLT and ONU are notified via the NEM, and the OTDR is used to localize the fault within the network of the branches. When the ONU activates the backup mode, the RFoG becomes activated and ready to send the data to the nearest ONU (backup ONU) within its coverage. Simultaneously, the SDN controller updates the network configuration to handle the failover scenario. For instance, if partial fiber degradation is detected, the SDN controller may initially attempt to reroute traffic within the primary path. In the event of a complete fiber cut to ONU\({}_{1}\), the ONU\({}_{1}\) and SDN controller trigger the RFoG backup mechanism, routing data through ONU\({}_{2}\). This multi-layered approach ensures robustness against various types of failures. Since there is no direct connection link between the affected ONU and OLT, a mechanism must be used so that the nearest ONU (backup ONU) can differentiate the incoming data from the OLT and send it to the affected ONU via RFoG. Similarly, the OLT needs to know that the data comes from the affected ONU. This can be achieved using data tagging such as a virtual local area network (VLAN). In the proposed architecture, the VLAN tag table is established in the OLT and ONUs. This table can be changed over time and updated using the SDN controller, which dynamically updates the VLAN tag table and configurations based on network changes and faults. This ensures that the OLT and ONUs will map VLAN tags to their respective destinations. Table 2 shows an example of the VLAN tag table. Figure 4: The smart resilience architecture in NG-EPON.
# Research on the Attitude Control Strategy of TBM Digging with V-Shaped Propulsion System
## 3 TBM Digging Attitude Control Strategy
### Digging Attitude Control Strategy Design  
Based on the mathematical model of the V-TBM propulsion system and the nonlinear controller, along with the analysis of both the forward and inverse kinematics, the tunneling attitude control strategy system is shown in Figure 4. The system initially preprocesses the tunnel design axis to determine the positional change in the front shield platform, then inputs the platform position information into the inverse kinematics model to calculate the desired displacement values for the eight propulsion hydraulic cylinders. This displacement serves as the desired signal to actuate the hydraulic cylinders of the propulsion system, and closed-loop control of the cylinder displacement is achieved through a nonlinear controller. Meanwhile, the actual displacement of the propulsion hydraulic cylinders is fed back to the forward kinematics in real time to obtain the actual position parameters of the shield platform, enabling position calibration and adjustment.
## 2 The Proposed Method
### Scenario-Based Noise Removal
#### 2.2.2 BLF-WLS Model  
The bilateral function is also known as the \"edge preserving\" filter because of its ability to preserve the image's edge information. Maintaining as much of the sample picture's edge information as possible is a key objective of image denoising because, due to the peculiarities of the samples in pathological hyperspectral images, biometric traits can still be recovered at the image's edge. Our proposed BLF-WLS model is to embed the bilateral filter function as a weight into the weighted least squares model, so that the image can achieve the purpose of preserving edge information to a greater extent. The distinctive attribute of the BLF-WLS filtering model is that the edge information is kept as the optimization objective to maximize the retention of the edge biometric data. The BLF-WLS model is innovative in that it incorporates picture bilateral filtering results as weights into WLS optimization objectives. This is carried out in an effort to preserve as much of the edge information of medical tumor images as possible, since edge biometric traits frequently serve as a crucial point of reference for the precision of medical image diagnosis. The BLF-WLS model is shown in Formula (4):  
\[\underset{\chi}{\text{min}}\sum_{(i,j)}\left(\left(\chi_{(i,j)}-\gamma_{(i,j) }\right)^{2}+\lambda\sum_{s\in\{i,j\}}\left(\omega_{x,(i,j)}\left(\
abla \chi_{\ast,(i,j)}\right)-\left(f_{BLF}\left(\omega_{y,(i,j)}(\
abla\gamma_{ \ast})\right)\right)_{(i,j)}\right)^{2}\right) \tag{4}\]  
\(\chi_{(i,j)}\)--Input Image  
\(\gamma_{(i,j)}\)--Output Image  
\(\omega_{x,(i,j)}\), \(\omega_{y,(i,j)}\)--Weighted value  
\(\
abla{X_{\ast}}_{(i,j)}\), \(\
abla{Y_{\ast}}_{(i,j)}\)--Image gradient  
\((i,j)\) represents the pixel position, \(\chi\), \(\gamma\) represent the original image and the output image, respectively, and the matrix representation of optimization objectives is shown as (5).  
\[(\chi-\gamma)^{T}(\chi-\gamma)+\lambda(\chi^{T}{D_{x}}^{T}A_{x}D_{x}\chi+f_{ BLF}(\gamma^{T}{D_{y}}^{T}A_{y}D_{y}\gamma)) \tag{5}\]  
The image size is \(700\times 600\). Let us introduce a variable \(p=700\times 600\); then, \(p\) is a scalar representing the total number of pixels in the image. A diagonal matrix where \(A_{x}\) and \(A_{y}\) are \(k\times k\), \(D_{x}\) and \(D_{y}\) together is the matrix representation of the discrete difference operator,and \(f_{BLF}\) represents the weight of the bilateral operator. By setting the optimization goal, the gradient is 0, and the equation can be obtained as follows:  
\[(I+\lambda f_{BLF}(L_{\gamma}))\chi=\gamma \tag{6}\]  
In the formula, \(L_{\gamma}=D^{T}A_{x}D_{x}+D^{T}A_{y}D_{y}\); as for the gradient design of weights, we did not use the logarithmic brightness of WLS algorithm, but the gradient of the original image, which has been proven to achieve a smoother effect more quickly. The weight design is as follows:  
\[\alpha_{x,(i,j)}=f_{BLF}\Big{(}\Big{|}\
abla\chi_{x,(i,j)}\Big{|}^{\alpha}+ \varepsilon\Big{)}^{-1},\;\alpha_{y,(i,j)}=f_{BLF}\Big{(}\Big{|}\
abla\chi_{ y,(i,j)}\Big{|}^{\alpha}+\varepsilon\Big{)}^{-1} \tag{7}\]
## 3 The Proposed Method
### Watermark Extraction  
The watermark extraction is quite simple and blind. Neither the original object nor the watermark is needed in the extraction process; only the secret keys (key_1, key_2) that represent the generator of the watermark and the quantization step, respectively, are needed. First, multiresolution analysis is applied to the watermarked 3D mesh by performing wavelet decomposition until the level \(I\) is reached. Afterwards, the mesh saliency is carried out to the watermarked mesh and the modified wavelet coefficients are defined according to the threshold \(T_{r}\). Next, the norms of the modified wavelet coefficients are re-quantized using the same quantizer as the embedding. Finally, the watermark bitsare extracted using Equation (5). Figure 5 illustrates the watermark extraction process that is presented in detail in Algorithm 2.  
```
0: Watermarked 3D mesh, key_1, key_2.
0: Watermark.
1. Do wavelet analysis starting from the watermarked mesh until level \(I\) is reached.
2. Calculate mesh saliency and extract the modified wavelet coefficients according to the threshold \(T_{r}\).
3. Calculate the norms of the extracted wavelet coefficients and apply QIM quantization.
4. Extract the watermark bits using Equation (5).
```  
**Algorithm 2**Watermark extracting.
## 4 Simulation Experiment Results and Discussion
### The Influence of DM on Conventional and Polarization Modulation  
The combination of PM with DM is compared against the QPSK plus DM scheme. In this comparison, the desired reception direction is set to 60\({}^{\circ}\), and the azimuth scan range is defined as 40\({}^{\circ}\)-80\({}^{\circ}\). The simulation results illustrating this comparison are presented in Figure 11.  
From the above Figure 11, it is evident that the overall BER performance of the PM plus DM scheme is superior to that of the QPSK plus DM scheme under the same azimuthal parameters. Locally, the BER curve for PM plus DM is steeper and rises more rapidly near the desired reception direction.  
Figure 11: BER of hybrid modulation. (**a**) PM + DM. (**b**) QPSK + DM.  
Figure 10: Three-dimensional constellation diagram of 4-PM signal after MP-WFFFT transformation.
## 3 Test Results and Analysis
### Conventional Triaxial Compression Strength
#### 3.2.3 The Nonlinear Characteristics of the Shear Strength of Gravel Soil
Figure 9 illustrates that as the xanthan content is increased to 6%, both the cohesion (\(C_{c}\)) and the internal friction angle (\(\varphi_{c}\)) demonstrate a linear increase, with increases of approximately 179.39% and 14.12%, respectively. The addition of xanthan gum to gravel soil results in enhanced cohesion, with the internal friction angle exhibiting a comparatively lesser degree of improvement. This is attributed to the gum's pronounced adhesive characteristics when in contact with water. This results in a discernible cementing effect between soil particles, with gel products forming a network structure that fills voids between coarse and fine particles, thereby binding them together. Conversely, as the lignin content increases to 6%, both the cohesion and the angle of internal friction initially increase and then decrease. The optimum shear parameters are achieved at a 4% content, where the cohesion and angle of internal friction increase by 85.79% and 12.26%, respectively. Therefore, it can be seen that the effect of xanthan gum on the increase in the cohesion of gravel soil is better than that of lignin on cohesion, while for the effect on the angle of internal friction, the two are not much different. The higher the perimeter pressure, the greater the cohesion and angle of internal friction of the two improved soils, while the difference between the cohesion \(C_{c}\) and angle of internal friction \(\varphi_{c}\) of xanthan gum and lignin dosing improved soils under a perimeter pressure of 300 kPa narrowed down, which suggests that, at higher perimeter pressures, the interstitial space of the rock and soil particles decreases, making the amount of xanthan gum and lignin filling decrease, which leads to a decrease in the amount of increase in the cohesion and the angle of friction of the \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Sample**} & \multicolumn{2}{c}{**Confining**} & \multirow{2}{*}{\(k^{\prime}\)} & \multirow{2}{*}{\(a^{\prime}\)} & \multirow{2}{*}{\(R^{2}\)} & \multirow{2}{*}{\(C_{c}\)/(kPa)} & \multirow{2}{*}{\(\varphi_{c}\)/\(c^{\circ}\)} \\  & **Pressure/(kPa)** & & & & & \\ \hline \multirow{3}{*}{2\% Lignin} & 100 & 0.502 & 0.529 & & 62.00 & 30.21 \\  & 200 & 0.518 & 0.553 & 0.999 & 65.50 & 31.23 \\  & 300 & 0.523 & 0.615 & & 73.12 & 31.58 \\ \hline \multirow{3}{*}{4\% Lignin} & 100 & 0.527 & 0.545 & & 64.97 & 31.85 \\  & 200 & 0.549 & 0.625 & 0.999 & 75.75 & 33.32 \\  & 300 & 0.562 & 0.690 & & 84.52 & 34.23 \\ \hline \multirow{3}{*}{6\% Lignin} & 100 & 0.519 & 0.539 & & 63.88 & 31.28 \\  & 200 & 0.527 & 0.599 & 0.997 & 71.40 & 31.82 \\  & 300 & 0.541 & 0.656 & & 79.01 & 32.77 \\ \hline \end{tabular}
\end{table} 
Table 6: _Cont._  
Figure 9: Relationship between the strength index and the dosage of xanthan gum and lignin. crushed stone soil, and, thus, that, under high perimeter pressures, the xanthan gum and lignin dosing has a weakening effect on increasing the shear strength of the soil.