esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Uncertainty Propagation Approach of Constellation Phase
### Product-Based Least-Squares Surface FITTING Approach  
Multiple sets of Monte Carlo simulations with different orbital propagation times \(t\) and position (velocity) standard deviations \(\sigma_{r}\) (\(\sigma_{r}\)) are selected. The time series is denoted as \(t\) (days), the position or velocity standard deviation series is denoted as \(\sigma\) (m or m/s), the standard deviation of the phase difference obtained by the Monte Carlo approach is denoted as \(\phi\) (rad), and \(t\), \(\sigma\), \(\phi\) are all row vectors, which approximately form a three dimensional surface. The set of functions \(\{\varphi_{p}(t)\psi_{q}(\sigma)\}\) is used as the basis function for the product-type least-squares fit [31], where  
\[\begin{split}&\varphi_{p}(t)=t^{p},\,p=0,1,\ldots,\text{N},\\ &\psi_{q}(\sigma)=\sigma^{q},\,q=0,1,\ldots,\text{M}.\end{split} \tag{21}\]  
Construct a surface with \(c_{pq}\) as parameter, which can be expressed as  
\[\Gamma(t,\sigma)=\sum_{q=0}^{\text{N}}\sum_{p=0}^{\text{M}}c_{pq}\varphi_{p}(t )\psi_{q}(\sigma). \tag{22}\]  
where, the coefficient \(c_{pq}\) is a matrix form given by  
\[\mathbf{C}=(\mathbf{B}^{\text{T}}\mathbf{B})^{-1}\mathbf{B}^{\text{T}}\mathbf{\phi}\mathbf{G}(\mathbf{G}^{ \text{T}}\mathbf{G})^{-1}, \tag{23}\]  
with  
\[\begin{split}\mathbf{B}&=[\varphi_{0}(\mathbf{t}^{\text{T} }),\,\ldots,\,\varphi_{\text{N}}(\mathbf{t}^{\text{T}})],\\ \mathbf{G}&=[\psi_{0}(\sigma^{\text{T}}),\,\ldots,\, \psi_{\text{M}}(\sigma^{\text{T}})].\end{split} \tag{24}\]  
In this way, an approximate function of the standard deviation of the initial position velocity and the standard deviation of the phase difference can be obtained.
# An Improved Genetic Algorithm with Adaptive Variable Neighborhood Search for FJSP
## 4 Iga-Avns
### Adaptive Variable Neighborhood Search
#### 4.4.2 Flow of Adaptive Variable Neighborhood Search Algorithm  
Different from the previous VNS using a fixed search order, this paper proposes an adaptive neighborhood selection strategy based on neighborhood search performance. The adaptive strategy is to learn the validity of each neighborhood search in the current optimization algorithm as the iteration proceeds. In order to promote effective competition between neighborhoods, the neighborhood method with a better search strategy has a higher probability to be used for the optimization of the algorithm. The steps of the adaptive neighborhood search algorithm are as follows and the flow chart is shown in Figure 5.  
Step 1: Initialize parameters--initial solution \(S\) (from elite population), number of iterations \(P\), the neighborhood structure \(N_{k}\) (\(k\) = 1, 2, 3), loop variable \(i\) = 1, the success and failure times of neighborhood search that are expressed as \(N_{s}\) = 0, \(N_{f}\) = 0;  
Step 2: Judge whether the termination criterion (\(i\) > \(P\)) is satisfied, and if so, output the optimal solution \(S^{*}\), otherwise go to Step 3;  
Step 3: If \(i\) < \(P\)/3, randomly select the neighborhood \(N_{k}\) for the search to get a new solution \(S^{\prime}\). Otherwise, calculate the probability \(\eta_{i}\) according to Equations (5) and (6) (select the larger \(\eta_{i}\) value), and select the neighborhood \(N_{k}\) for neighborhood search to obtain a new solution \(S^{\prime}\);  
Step 4: If \(\text{f($S^{\prime}$)}\) < \(\text{f($S^{*}$)}\), \(S^{*}\) = \(S^{\prime}\), \(N_{s}\) = \(N_{s}\) + 1; otherwise, \(N_{f}\) = \(N_{f}\) + 1;  
Step 5: Update the \(\eta_{i}\) values of each neighborhood, \(i\) = \(i\) + 1. Go to Step 2.  
\[\zeta=\ \alpha\cdot\frac{N_{s}}{N_{s}\ +\ N_{f}} \tag{5}\]  
\[\eta_{i}=\ \frac{\zeta_{i}}{\sum_{k\ =\ 1}^{3}\zeta_{k}} \tag{6}\]  
\[\alpha=\frac{f(s^{*})}{f(c)} \tag{7}\]  
The parameter \(\zeta\) is used to the measure the improvement degree of the chromosome. \(\alpha\) denotes the relative fitness value, \(f(S^{*})\) is the global optimal solution obtained in the current elite library, \(f(c)\) is the fitness value of the chromosome in the current neighborhood search, and the probability \(\eta_{i}\) of each neighborhood is calculated according to \(\zeta\) in the search process.
# Analytical Approach in the Development of RF MEMS Switches
## 2 Analytical Approach to the Development of RF MEMS Switches of Electrostatic Type  
The analytical approach in the development of RF MEMS switches has the following structure, shown in Figure 1.
## 7 Limitations and Future Research Directions  
The findings suggest that PCA may not be universally applicable across all phases of product design, particularly in industries where required characteristics are less dominant among feature characteristics such as the healthcare/medical industry. For the limitations observed with Dataset X2, PCA may not be suitable for phases that require a nuanced understanding of diverse feature requirements. In this context, product design teamsshould consider using other techniques alongside PCA to capture the detailed needs of the product design process.  
Additionally, assumptions made during PCA application and potential biases in the data used could impact the results. Ensuring data quality and considering alternative methods can help mitigate these limitations.  
Future research could explore the underlying factors contributing to the varying compatibility of PCA with different industrial sectors and phases of product design. Detailed studies focusing on specific industries could provide deeper insights into the impact of this methodology. Additionally, research should examine the impact of PCA-AHP-QFD on different dimensions such as cost and time-to-market to develop a more comprehensive understanding of its benefits and limitations.  
Conceptualization, P.A. and R.-H.L.; methodology, P.A.; software, P.A.; validation, P.A., R.-H.L. and C.-L.C.; formal analysis, P.A.; investigation, P.A.; resources, P.A.; data curation, P.A.; writing--original draft preparation, P.A.; writing--review and editing, P.A., R.-H.L. and C.-L.C.; visualization. P.A., R.-H.L. and C.-L.C.; supervision, P.A. and R.-H.L.; project administration, P.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The methodology and results coding are available upon request from the corresponding author via email.  
The author expresses gratitude to the original sources of the AHP-QFD matrix, as referenced in [23; 24; 30], for laying the foundation for this study. Appreciation is extended to the developers of the open-source code within the sklearn.decomposition module in Python language, which significantly contributed to the analytical aspects of this research. Special thanks are due to National Taipei University of Technology for providing access to invaluable online library resources, facilitating comprehensive literature review and research. Furthermore, heartfelt appreciation is extended to all members of the Hong-Yue Technology Research Building Room 532 Laboratory, particularly the Enterprise Decision Management Research Center, for their unwavering support in administration and facility provision throughout the duration of this study. Additionally, since thanks go to the interview questionnaire participants for their valuable insights and feedback.  
The authors declare no conflicts of interest.
## 5 Conclusions and Future Developments
Indeed, actual yaw rates and side-slip angles exhibit lower errors with respect to target values using LQR and SMC stability program solutions. In particular, in the _Step Steer_ maneuver (Figure 6), it is evident how the tracking efforts exhibit different control actions between developed ESP solutions and the Commercial one, achieving better stability and dynamic performance of the car. This is also shown in the _Sine Steer_ test of Figure 9. Furthermore, the need for stability control is essential: the driver with a Non-Controlled vehicle is unable to perform the maneuver without losing control of the car; instead, the configuration with an ESP ensures one can maintain stability and maneuverability. The differences between the proposed optimal control solution can be seen in the _Lane Change_ test. In addition, to improve the tracking performances of both yaw rate and side-slip angle, this maneuver shows also achievements allowed by the exploitation of traction moments. In Figure 12, both SMC and LQR assist the driver, enhancing vehicle handling aspects with respect to the Commercial-controller. However, the sliding approach allows a smoother response of the car to the driver's intentions, letting it maintain the target longitudinal vehicle entrance speed and producing desired yaw moment by the usage of traction command and steering, in addition to conventional differential braking technique. The optimal control solutions investigated in this activity could be objects of further improvement. Firstly, the definition of a gain scheduling technique with respect to vehicle speed and the tire-road contact patch conditions (e.g., cornering stiffness) could lead to higher tracking performances, both for linear and non-linear dynamical behavior. Moreover, a robustness analysis with respect to estimation uncertainties is necessary to ensure stability in different driving conditions. Conceptualization, C.A. and L.P.; Methodology, F.A., M.M. and T.F. ; Validation, L.P.; Formal analysis, M.M., T.F. and L.B. ; Writing--original draft, F.A. ; Writing--review & editing, M.M., T.F., L.P. and L.B. ; Supervision, C.A., R.C. and M.P. ; Project administration, R.C. ; Funding acquisition, R.C.. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The authors declare no conflict of interest.
## 3 UWA Receiver Structure
### Signal Detection in UWA Environments
In addition, the MMSE equalizer requires a priori SNR, which is difficult to obtain in time-varying UWA channels. When the frequency selectivity becomes strong, with imperfectly estimated channels, both ZF and MMSE equalizers cannot recover the symbols effectively [40]. However, it is still necessary to develop a more effective frequency domain equalizer in underwater acoustic channels because of the attractive low complexity. Variable NN structures provide new solutions to such interference elimination problems. The NN now has been proved to learn one or more nonlinear processes well with a proper structure [33]. With an intelligent interference simulating model [41], it is possible to train an equalization network offline, which can be applied independently online without extra a priori environment information. Figure 3: A decision region of the QPSK constellation.
# Solar-Supplied Satellite Internet Access Point for the Internet of Things in Remote Areas
## 5 Discussions
### Costs of Implementation  
While the costs of equipment and data plans are detailed in Tables 3-5, other costs must also be considered in actual deployment. The costs include 1. Arrangement for initial installation: since the proposed satellite access point is provided by solar energy and uses a satellite phone as a terminal, its installation is simple. As long as it is placed in a location of interest and powered on, the Wi-Fi hotspot and satellite connection will be ready accordingly. Therefore, the cost of the initial installation is one-time and minimal.
2. Need for remote maintenance services: as there is a single-board computer in the proposed satellite access point, remote configuration for the hotspot settings is feasible. However, if unexpected hardware errors occur, arrangement for the repair or maintenance work is required.
3. Arrangement for the renewal of SIM cards: if prepaid SIM cards are used, arrangements are required for their renewal. To reduce this kind of arrangement, 365-day prepaid SIM cards or monthly (or even yearly) service plans and automatic payments can be considered.
4. Fee to national agencies: in some countries, a fee has to be paid to the national agencies responsible for satellite services.  
Even though the above costs are required, they are lower than other methods (e.g., VSATs mentioned in Section 2.4 and cabling a physical network mentioned in Section 1), and the proposed satellite access point is a feasible approach in providing internet connectivity to the IoT in remote areas.
# Unified Ciphertext-Policy Weighted Attribute-Based Encryption for Sharing Data in Cloud Computing
## 1 Introduction
### Problem Statement  
In practical applications [7; 22], different data providers share their own data with weighted access policies, and these policies may have a mutual sub-policy. A simple instance is given to elucidate this issue.  
As illustrated in Figure 1, the data providers have data \(M_{1}\) and \(M_{2}\) to share separately. The weighted access policy of \(M_{1}\) is \(\Gamma_{1}\)[Attr3(3) AND (Attr1(2) AND Attr2(3))]. The weighted access policy of \(M_{2}\) is \(\Gamma_{2}\)[Attr4(2) AND (Attr1(2) AND Attr2(3))]. We notice that there is a mutual sub-policy [Attr1(2) AND Attr2(3)] between \(\Gamma_{1}\) and \(\Gamma_{2}\), and this means that the encryptions of \(M_{1}\) and \(M_{2}\) have some processes of repetition. This provides the possibility of improving the efficiency of the data sharing.
# Study on Compressive Strength and Water Permeability of Steel Slag-Fly Ash Mixed Permeable Brick
## 3 Results and Discussion
### Effects of Different Variables on Compressive Strength
When the proportion of the fly ash increases, the overall mechanical properties of the shaped permeable bricks are closer to the fly ash, so the compressive strength decreases [9]. Therefore, in subsequent tests, the proportion of fly ash was controlled to 30%. Porosity is the main parameter reflecting the structure of permeable concrete. The porosity can also basically reflect the water permeability of permeable concrete. At the same time, porosity also provides direct information for the mix design. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Samples**} & \multirow{2}{*}{**Fly Ash Content (\%)**} & \multicolumn{2}{c}{**Compressive Strength**} & \multicolumn{2}{c}{**Compressive Strength**} \\  & & **under 7 Days Curing** & **under 28 Days Curing** \\  & & **(MPa)** & **(MPa)** \\ \hline
1 & 55.2 & 19.69 & 20.12 \\
2 & 50.3 & 20.36 & 21.68 \\
3 & 45.6 & 22.54 & 22.93 \\
4 & 40.4 & 23.05 & 23.63 \\
5 & 35.7 & 23.54 & 24.63 \\
6 & 30.5 & 25.63 & 26.52 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Compressive strength at different fly ash contents. Figure 3: Compressive strength at different steel slag contents. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Samples**} & \multirow{2}{*}{**Steel Slag**} & \multirow{2}{*}{**Compressive Strength**} & \multicolumn{2}{c}{**Compressive Strength under**} \\  & & **under 7 Days Curing (MPa)** & **28 Days Curing (MPa)** \\ \hline
1 & 5 & 19.69 & 21.06 \\
2 & 5.2 & 21.68 & 21.35 \\
3 & 5.5 & 22.57 & 23.68 \\
4 & 5.8 & 21.86 & 23.63 \\
5 & 6.1 & 25.54 & 26.23 \\
6 & 6.5 & 25.63 & 26.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Compressive strength at different steel slag contents. It can be seen from Table 7 and Figure 5 that the compressive strength of the permeable bricks decreases with increasing porosity. This is because when the porosity increases, the contact points and contact faces between the various components of the permeable brick decrease, so it is liable to be destroyed under external force [12]. Fitting the data in Figure 5, it is found that there is an approximate relationship between the compressive strength and porosity of the permeable brick. \[F=35.41-0.363p-0.02p^{2} \tag{5}\]  
where \(F\) is the compressive strength of permeable brick and \(p\) is the observed porosity.
###### Abstract
In order to study the influence of the installation position and the installation angle of the horizontal propeller of the remote-controlled unmanned submersible in the three motions of ROV surge, sway, and yaw, [PERSON] et al. [22] determined the installation angle of the propeller according to the specific working environment of the small ROV and proposed a horizontal propeller thrust distribution method combining power normalization and sequential quadratic programming. The rationality and effectiveness of this method for thrust distribution were verified through simulation. [PERSON] et al. [23] designed and implemented an adaptive thrust allocation algorithm in an already powerful control system to maximize the operational efficiency of the continuous split hull underwater vehicle. [PERSON] et al. [24] used a genetic algorithm to distribute the thrust of the thruster according to the vector arrangement of the thruster of the designed ROV and achieved reasonable thrust distribution. In order to reduce the power consumption and hardware wear of ship dynamic positioning, [PERSON] et al. [25] proposed an improved dung beetle thrust allocation algorithm (IDBO) to solve the problems of low accuracy and ease of falling into local extremum points in the traditional thrust allocation algorithm. The simulation results show that the convergence speed of the algorithm is better than that of the contrast swarm intelligence algorithm, and the thrust allocation's accuracy and energy consumption are also significantly better than those of all of the comparison algorithms. In the above research, the thrusters of the underwater vehicle are arranged in a relatively simple horizontal and vertical arrangement. The thrust component and the torque generated by each thruster are less coupled. The thruster arrangement adopted in this paper means that the thrust and the torque generated by each thruster have greater coupling. Because the designed ROV has a chassis structure and the structure is more complex, it is more important to solve the thrust distribution problem. In this paper, the direct logic method and the sequential quadratic programming method are used to compare the thrust allocation, and the mathematical model of the propulsion system is established. The thrust allocation simulation model is established in MATLAB and Unity 3D, and the simulation and analysis are carried out. Finally, the underwater attitude control experiment based on thrust allocation and the underwater practical scene application experiment are carried out. Through various simulations and experiments, the feasibility of the sequential quadratic programming method for thrust distribution optimization is verified, and the entire ROV system can meet the expected design requirements.
# Energy Efficient Framework for a AIoT Cardiac Arrhythmia Detection System Wearable during Sport
## 4 Experimental Results
### Power Consumption and Battery Extension Analysis  
Power consumption is evaluated at the circuit level. \(N_{TS}=66\) samples, 1000 measurements each, are implemented during the 45 min training. Two different sample periods were used, in a high-intensity section of 21 min with \(N_{HS}=42\) samples, and in a low-intensity during the first and last 19 and 5 min, with \(N_{LS}=24\). BLE advertising interval increase to optimize the current consumption by incorporating empty packets to keep the connection alive. The transmit power was also adjusted to 0 dBm, which is enough to cover the 10 to 15 m range along the training, and also, to maintain the power consumption. The prototype's current consumption (mA) analysis is the following:  
\[\begin{split} Q=N_{TS}(I_{ECG}T_{ECG}+I_{PRO}T_{PRO}+I_{STO}T_{STO }+...\\ I_{BLE}T_{BLE})+N_{LS}(I_{S}T_{S1})+N_{HS}(I_{S}T_{S2})\end{split} \tag{1}\]  
where \(I_{ECG}=1.5\)\(\upmu\)A is the MCU average current consumption in active mode with the analog font-end at each data acquisition period \(T_{ECG}\) of 4.7 s, \(I_{BLE}\) requires an average of 0.357 mA to send data packets in \(T_{BLE}=10\) s, \(I_{STO}=1.53\)\(\upmu\)A is the current to data store in \(T_{STO}=0.1\) s, \(I_{PRO}=1.5\)\(\upmu\)A is the current to process the data in \(T_{PRO}=0.2\) s, and \(I_{S}=0.5\)\(\upmu\)A is the average current consumption of the MCU in sleep mode for the sleep periods of \(T_{S1}=45\) s and \(T_{S2}=15\) s.  
Considering a session discharge of \(Q=237.5\) mA for 45 min training (or \(Q=316.7\) mAh), and a 3.7 V battery with a nominal capacity of 2600 mAh and 90% DC-DC converter efficiency, the battery duration can be estimated (without energy harvesting) for approximately 7.3885 h, or 9.8513 training sessions. The selected mono-crystalline PV module of dimension \(113\times 89\times 5\) mm for the intelligent ECG device produces 157.34 mAh at a typical STC (i.e., 1000 W/m\({}^{2}\) and a cell temperature of 25 \({}^{\circ}\)C). Regarding a capture efficiency of 35% for a wearable operation of the solar PV module [37; 38], the harvester system provides \(\approx\)55.1 mAh, which means a battery life extension for 1.7 more training sessions without the need for manually recharging or replacing the battery.
## 4 Results
### Result Analysis  
The comparison of the magnetic interference waveforms before and after compensation in Figures 6 and 7 shows that the 18 compensation coefficients only based on INS/GPS information can also effectively compensate for the noise generated by the aircraft. The compensation effect is slightly lower than 18 compensation coefficients only based on fluxgate magnetometer information, but the difference is not obvious. It is considered to cancel the fluxgate magnetometer and use INS/GPS information in some application scenarios of low power consumption and light load.  
There are still certain amounts of residual interference in both. It may be caused by fluxgate and INS/GPS measurement error or IGRF modeling error. The conjunctive compensation model can achieve a significant compensation effect through combining the INS information and fluxgate magnetometer information to construct the conjunctive compensation model.  
As shown in Figures 8 and 9, in the 0.04-0.3 Hz band of concerning, the magnetic interference spectral density of conjunctive compensation is significantly less than that of the traditional compensation method using only fluxgate information.  
As can be seen from Tables 1 and 2, the compensation effect of the conjunctive compensation model is better than that only based on fluxgate magnetometer information 30-60%. The IR is significantly improved and it can be compensated in real time. The experimental data and their processing results show that this method can significantly reduce the residual magnetic interference as an improvement of the traditional compensation method.  
can be estimated by designing flight trajectory and flight actions. Will this help to correct fluxgate errors? These are all possible directions for future research.