esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In order to obtain the adsorption mechanism and adsorption structures of Rare Earth Elements (REEs) ions adsorbed onto layered double hydroxides (LDH), the adsorption performance of LDH and ethylenediaminenetetraacetic acid (EDTA) intercalated LDH for REEs was investigated by batch experiments and regeneration studies. In addition to adsorption capacity, the partition coefficient (PC) was also evaluated to assess their true performance metrics. The adsorption capacity of LDH increases from 24.9 \(\upmu\)g-g\({}^{-1}\) to 145 \(\upmu\)g-g\({}^{-1}\) for Eu, and from 20.8 \(\upmu\)g-g\({}^{-1}\) to 124 \(\upmu\)g-g\({}^{-1}\) for La by intercalating EDTA in this work; and PC increases from 45.5 \(\upmu\)g-g\({}^{-1}\)-uM\({}^{-1}\) to 834 \(\upmu\)g-g\({}^{-1}\)-uM\({}^{-1}\) for Eu, and from 33.6 \(\upmu\)g-g\({}^{-1}\)-uM\({}^{-1}\) to 405 \(\upmu\)g-g\({}^{-1}\)-uM\({}^{-1}\) for La. Comparison of the data indicates that the adsorption affinity of EDTA-intercalated LDH is better than that of precursor LDH no matter whether the concept of adsorption capacity or that of the PC was used. The prepared adsorbent was characterized by XRD, SEM-EDS and FT-IR techniques. Moreover, quantum chemistry calculations were also performed using the GAUSSIAN09 program package. In this calculation, the molecular locally stable state structures were optimized by density functional theory (DFT). Both the quantum chemistry calculations and the experimental data showed that REEs ions adsorbed by EDTA-intercalated LDH are more stable than those adsorbed by precursor LDH. Furthermore, the calculation results of adsorption and desorption rates show that adsorption rates are larger for Eu(III) than for La(III), which agrees with the experimental result that Eu(III) has a higher adsorption ability under the same conditions. The LDHs synthesized in this work have a high affinity for removing REEs ions. Keywords: layered double hydroxides; EDTA-intercalated LDHs; quantum chemistry calculation; REE ions; adsorption mechanism; desorption mechanism +
Footnote †: journal: Applied Soft. 2019
## 4 Discussion
### The Necessity of Incident Angle Modifier  
The solar radiation incident on the PV module is more likely to be reflected off the surface as the incident angle increases. PR decreased by considering the incident angle modifier regardless of the season. Table 2 shows the annual irradiance incident on each PV module and the effective insolation rate. The two-axis tracking Si module was slightly affected by the incident angle modifier, and the effective solar radiation rate was 99.69%. Next, it was 98.75% with fixed Si and 98.19% with the IMM module. Since the PV module on the two-axis tracking system can always track the sun, there is no direct irradiance loss. On the other hand, when installing the PV tilted, the direct irradiance loss is tremendous. The incident angle modifier of the IMM was worse than Si. Therefore, the effective insolation rate was, from highest to lowest, the tracking silicon, fixed silicon, and fixed IMM. Without considering the incident angle modifier, overestimating PV output may occur.
## 2 Unified Algorithm of Finite Memory Structure Filter and Smoother
The deadbeat property means that the unified algorithm of FMS filter and smoother \(\hat{X}_{i}\) in (13) tracks exactly the current state \(x_{i}\) and the lagged state \(x_{i-d}\) at every time for noise-free systems although the proposed unified algorithm has been designed assuming a couple noises such as \(w_{i}\) and \(v_{i}\) in the discrete-time state-space model (1). Since the deadbeat property indicates finite convergence time and fast tracking ability, of the proposed unified algorithm of FMS filter and smoother. Thus, it can be expected that the proposed unified algorithm of FMS filter and smoother might be appropriate for a variety of applications requiring fast tracking capability such as fault detection and diagnosis for dynamic process systems as well as maneuver detection and target tracking for indoor positioning systems. Moreover, in contrast to the recursive IMS filter such as the Kalman filter, the proposed unified algorithm can have intrinsic robustness due to its finite memory structure. This means that the proposed unified algorithm can be more robust against round-off errors, mismodeling and temporary uncertainties. The on-line computational complexity of the proposed unified algorithm is discussed. To simply compare the computational complexity of the proposed unified algorithm and the IMS filter, the measurement \(z_{i}\) is assumed as a scalar-valued (i.e., \(q=1\)). In the case of the IMS filter such as the fixed-lag Kalman smoothing filter, the coefficient values for fixed-lag Kalman filtering and fixed-lag smoothing algorithms must be always computed for \(d\) iterations by on-line computing before inputting any data to the fixed-lag Kalman smoothing filter algorithm stage. Thus, the IMS filter such as the fixed-lag Kalman smoothing filter with \(n\)th dimension can be done with \(4n^{3}+8n^{2}+6n+1\) flops as shown in [26]. On the other hand, as mentioned before, the gain matrix (14) of the unified algorithm requires only once computation on the interval \([0,M]\) and then is used for all windows. Thus, FMS filter and smoother with \(n\)th dimension in the proposed unified algorithm can be done respectively with \(n\times M\) flops as shown in Table 1. Thus, as the dimension grows, the on-line computational complexity of the proposed unified algorithm is shown to be much less than that of the fixed-lag Kalman smoothing filter. However, the proposed unified algorithm requires the memory with size of \(M\) to store finite measurements on the most recent window \([i-M,i]\) and the memory shift with \(M-1\) operations. On the other hand, the fixed-lag Kalman smoothing filter requires to store only intermediate estimates with size of \(d\). Since \(M>d\), the fixed-lag Kalman smoothing filter can be better than the proposed unified algorithm in terms of the memory management.
# Optimizing Roller Design to Improve Web Strain Uniformity in Roll-to-Roll Process
## 3 Roller Design Optimization for Minimizing Strain Deviation
### Roller for Compensating Strain Deviation  
According to the FEA and experimental results, the strain in the MD of the web was high in the center of the CD when a uniform tension was applied to the conventional roller. The strain deviation in the MD can be compensated by adjusting the contact stiffness between the roller and the film. If the contact stiffness decreases toward the roller end, the relatively low strain increases; thus, the strain deviation in the web is compensated.  
Figure 5: Schematic of the web strain measurement method (top view) with a conventional roller.  
Figure 6: Strain measurement results along the cross direction (CD) of conventional roller.  
To test this concept with an actual roller, a roller with a convex core was chosen and the roller was wrapped in an elastic material (rubber skin). Hence, the core is rigid and the skin is flexible. As shown in Figure 7, the contact stiffness between the web and roller is greatest in the center and decreases toward each side. The total diameter of the core and skin assembly is constant to prevent wrinkling of the web. Since the core of the roller is convex, the roller was named a \"barrel roller\".  
We then used the FE model to optimize the design of the roller shown in Figure 7, focusing on the strain distribution in the MD 50 mm from the contact line. The goal of the optimization was to minimize the difference between the maximum and the minimum of the strain distribution in the MD along the CD.
## 1 Introduction
### Torsional Stiffness Analysis of the Laminated Membrane Coupling and the Lamination Group  
In this section, the driven flange is fixed and a torque of 3000 N\(\cdot\)m is applied on the active flange. Then the load sub-step method, which is divided into five sub-steps, is applied to analyze the torsional stiffness of the laminated membrane coupling and the lamination group.  
The end face of the driven flange is set to fixed support, and two load steps are set for load application, as shown in Figure 13. On the sides of the six bolts, the 'bolt pre-tightening force of 15 kN is set' in the first step, and 'lock is set' in the second step. Tabular data is used to load the end face of the active flange. The first step is set to 0, the second step is to apply a torque of 3000 N\(\cdot\)m, and the second step is to set 5 sub-steps, and the loading torque is 600 N\(\cdot\)m, 1200 N\(\cdot\)m, 1800 N\(\cdot\)m, 2400 N\(\cdot\)m and 3000 N\(\cdot\)m respectively.  
Through the simulation analysis, the corresponding rotational angular displacement of the laminated membrane coupling and the lamination group for torques of 600 N\(\cdot\)m, 1200 N\(\cdot\)m, 1800 N\(\cdot\)m, 2400 N\(\cdot\)m and 3000 N\(\cdot\)m can be obtained. Then the torsional stiffness can be calculated by Equation (1).  
Figure 13: Boundary condition settings for torsional stiffness simulation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter Name** & **Test Result** & **Simulation Result** & **Relative Error** \\ \hline Torsional stiffness \(K_{T}\) (N\(\cdot\)m/rad) & \(1.88\times 10^{6}\) & \(1.79\times 10^{6}\) & 4.8\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Torsional stiffness obtained from the numerical method and the experiment.  
Figure 14 shows the corresponding rotation angular displacement and torsional stiffness of the laminated membrane coupling and the coupling laminated group under different torque loads. It can be seen from the figure that the torque and rotational angular displacement of laminated membrane coupling has a certain nonlinear correlation within the range of 0-3000 N\(\cdot\)m, and the same is true for the coupling lamination group. For the laminated membrane coupling, the torsional stiffness is estimated to \(1.8\times 10^{6}\) N\(\cdot\)m/rad. Furthermore, for the coupling lamination group, the torsional stiffness is approximately \(5.45\times 10^{6}\) N\(\cdot\)m/rad. Since the stiffness value is large, it can be considered that the laminated membrane coupling is rigid along the torsional direction.
# Practical Use of Starlink Downlink Tones for Positioning
## 2 Signal Detection and Tracking
### Signal Recording
Finally, we emphasize the absence of a parabolic reflector that is traditionally associated with the LNB to capture Ku band signals. Figure 1: (a) Sketch of the recording chain, LNB down-convertor used without a parabolic reflector. (b) Photo of the experimental set-up. We recorded real-life data at a static location on the rooftop of a building to be near open-sky reception conditions. The LNB down-converts the 11,325 MHz carrier to 1575 MHz. The acquisition software was configured to sample the signal with a sampling rate of 2 MHz. Figure 2 gives an example of the spectrum of the recorded data computed over a signal burst of 14 ms. It shows that despite the absence of a parabolic reflector, many tones occupy the frequency interval surrounding 11,325 MHz with a magnitude well above the noise floor. The tones that are spaced ~44 kHz apart belong to the same satellite (indicated by arrows with the same color on the figure) and have different magnitudes. Typically, between one and several tones are captured for a satellite. We will show later that nine tones can be received for a single satellite.
# \"Network Sentiment\" Framework to Improve Security and Privacy for Smart Home
## 2 Related Works
An IoT security framework for Smart Home is introduced in [27] and a general threat model to recognize the vulnerabilities for IoT services against cyberattacks is analyzed. A smart home testbed is considered to monitor variables and control elements with different protocols such as Wi-Fi, ZigBee, etc. An IDS based on anomaly behavior analysis (ABA) of the end nodes operations allows to detect and classify a wide range of attacks against IoT devices, such as replay attacks, delay attacks, (DoS) or DDoS attacks, noise injections, etc. In [28] a security framework for home network is proposed with residential gateways as devices responsible for the exchange of information between the ISP infrastructure and the customer network to develop a vastly distributed IDS/IPS, enforcing preventive or corrective countermeasures, according to the instructions issued by the ISP. This security framework, as other papers in literature, foresee to move the security intelligence in the ISP. In our system, only the bare minimum necessary to calculate the users' similarity score is known by the ISP, while the actual countermeasures (i.e., the network configuration) is left to the smart home side (SHIELD Home (SH) device). This enables scalability (because the ISP does not have to address all the users' CPE details), fine-grained configuration (the SH device knows more precisely the actual user's network configuration), and it allows the user (if he is an expert) to override the security setup proposed by the ISP. Moreover, our solution provides a high degree of flexibility with respect to the different IoT networks, which are often user-owned and deployed. These networks are difficult to manage by the ISP in a \"pure\" centralized way and all the current approaches are not sufficiently reactive and dynamic to protect the smart environments adequately. As a matter of fact, in the current architectures attacking a network triggers only a local response, and it does not have any system-level reaction. We believe that our _network sentiment analysis_ fills the gap between actual network security techniques and a more coordinated reaction system.
## 1 Introduction  
In recent years, the time series modeling and prediction have been one of the most active research topics in the academic research and engineering practice [1; 2; 3; 4]. The time series is usually a chronological series of observed data (information) according to the time sequence, whose values are sampled at the invariable time intervals. Researchers often predict future changes based on the historical data. For example, according to the situation in the past or the current period of the market sales, the changes of stock prices, the population growth and the bank's deposit and withdrawal, the changes of the market sales, the changes of stock prices, the population growth and the bank's deposit and withdrawal in the future are predicted. The time series forecasting affects the life of people everywhere, so it has an important practical significance and research prospects in every field of today's society, which is also an important direction in the computer application field [5, 6, 7, 8].  
The bank cash flow forecasting management information system is designed to create a system management platform for the prediction and analysis of the commercial bank cash flow. It will realize the cash flow data statistics summary, the cash flow short-term and long-term prediction, the summary and statistical analysis comprehensively and scientifically of the business information, the operational information and the management information related to the commercial bank cash flow under three levels: secondary branches (Cash Operation Center), branch (Business Library) and Network. Its purpose is to provide effective data all levels of organization to analyze and assess cash business operation conditions. Also it will provide effective system management means for the cash operation managers and decision-making people at all levels.  
Aiming at the existed problem in the analysis of commercial bank cash flow, four time series prediction methods are used to set up the prediction models. The simulation results show the effectiveness of the proposed methods. The paper is organized as follows. In Section 2, the time series prediction methods of bank cash flow are introduced. The simulation experiments and results analysis are introduced in details in Section 3. Finally, the conclusion illustrates the last part.
## 4 Comparison of Different Wheel-Rail Contact Models
### Comparison under Seismic Conditions
#### 4.2.1. Wheel-Rail Contact Dynamics
To clarify the influence of different calculation formulae for the normal contact stiffness, the wheel-rail dynamic responses calculated based on Models 3 and 4 are compared, as plotted in Figure 17. For the contact in region 1, Figure 17a shows that based on Model 4, when contact occurs between the straight-line segment of the wheel tread and the R300 arc segment of the rail head, \(K_{nr}\) is maintained at 9.20 \(\times\) 10\({}^{10}\) N/m\({}^{3/2}\). When contact occurs between the R450 arc segment of the wheel tread and the R300 arc segment of the rail head,  
Figure 16: Time histories of wheel-rail dynamic responses based on Models 2 and 3: **(a)** Normal compression amount in region 1; **(b)** Normal compression amount in region 2; **(c)** Normal contact force in region 1; **(d)** Normal contact force in region 2; **(e)** Wheel jump amount. To clarify the significance of the effect of the normal compression algorithms, the normal contact stiffness algorithms, and the considerations of the number of the contact point, the maximum values of wheel-rail forces on both sides of the four wheelsets calculated on the basis of the different models are presented in Figure 18. The relative calculation errors can be expressed as: \(\Delta_{12}=(R_{1}-R_{2})/R_{2}\times 100\%\), \(\Delta_{23}=(R_{2}-R_{3})/R_{3}\times 100\%\) and \(\Delta_{34}=(R_{3}-R_{4})/R_{4}\times 100\%\). As shown in Figure 18, the algorithm for the normal compression amount has the most significant impact on the \(F_{Z}\) and \(F_{Y}\) solutions, with \(\Delta_{23}\) ranging from 48.97% to 339.50%. Regarding the impact of considering the wheel-rail contact point, considering only single-point contact may cause \(F_{Z}\) and \(F_{Y}\) to increase or  
Figure 17: Time histories of wheel-rail dynamic responses based on Models 3 and 4: (**a**) Normal contact stiffness in region 1; (**b**) Normal contact stiffness in region 2; (**c**) Normal compression amount in region 1; (**d**) Normal compression amount in region 2; (**e**) Normal contact force in region 1; (**f**) Normal contact force in region 2.  
decrease, with \(\Delta_{12}\) ranging from \(-15.26\%\) to \(35.00\%\). The impact of the normal contact stiffness algorithm is minimal, with \(\Delta_{34}\) ranging from \(6.04\%\) to \(23.55\%\).
## 4 Case Study
### System Failure Consequence Analysis  
#### 4.5.1 Determination of the Type of Failure Consequence and Their Weights  
Based on the literature, experts' opinions, and field survey, the consequences of the natural gas storage tank failure were divided into four categories: production interruption loss (C1), safety/health effect (C2), environment/ecological effect (C3), and equipment maintenance cost (C4) [53]. The weights of these four consequence types were first determined using the IVIF-AHP mentioned in Section 3.3.1. Specifically, the experts provide the relative importance of the consequence types in pairs in the form of linguistic terms (see Tables 11-13), and then these opinions are converted into the corresponding IVIFNs (see Tables 14-16). Through aggregation, the IVIFN for each consequence severity type was obtained. The results underwent a consistency test (\(CR=0.098<0.1\)) to ensure that the  
Figure 7: The probability of the critical event under different safety barriers.
## 3 Results
### Evaluations On The Automatically Generated Dataset
#### 3.1.4 Incomplete Dataset  
For the incomplete dataset, which refers to the situation that actions in an intrusion session are lost for various reasons. The missing action will not change the order of other actions in the session; however, it will cause serious problems in the data mining systems and the statistical systems, especially the sequential learning systems.  
The proposed method can solve the missing data problem by creating the association relations between actions. The influence relation of two actions becomes stronger if they always appear in the same session and are close to each other, otherwise, the influence relation will weaken. So, the missing action will not impact the learned patterns.  
The evaluation test is similar to the disordered data evaluation. The six types of pattern sessions are the same as the ones in Table 5. There are five missing-levels for each intrusion sessions. For instance, level 2 means two random actions of the pattern sessions are lost. For each missing level, 10 predictions are made for the particular input by the system. The prediction results are shown in Table 7.  
The average accuracy of the predictions made for particular input is 96%, which means the algorithm has a strong resistance to the incomplete data. The relation between the missing level and the prediction accuracy is shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c} \hline
**Inputs** & **Prediction Errors** & **Prediction Accuracy 1** \\ \hline a1 & (2,5,4,6,7) & 94.7\% \\ a11 & (0,4,1,4,5) & 96.9\% \\ a21 & (3,3,3,7,6) & 95.1\% \\ a31 & (1,2,4,5,7) & 95.8\% \\ a41 & (0,0,2,3,7) & 97.3\% \\ a51 & (0,2,5,4,7) & 96\% \\ \hline \end{tabular} \({}^{1}\) _accuracy_(_action_) = \((1-\frac{\text{Bactions wrongly predicted}}{\text{Bactions should be correctly predicted}})\times 100\%\).  
\end{table}
Table 7: Testing results based on the incomplete dataset.  
Figure 7: The prediction accuracy based on 5 missing levels.  
The algorithm will first analyze the training dataset and then load the testing dataset which is generated with the same parameters of the training dataset, all the datasets contain the pattern sessions randomly generated and the background sessions. The experimental results show that the missing actions can hardly impact the proposed algorithm. The average prediction accuracy has been kept above 93% when even 40% of the actions are missing.