esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Ionic liquids (ILs) have been reported to be good process aids for enhanced bitumen recovery from oil sands. However, after the extraction, some ionic liquids are left in the residual solids or solutions. Herein, a washing-ion exchange combined method has been designed for the removal of two imidazolium-based ILs, ([Bmim][BF\({}_{4}\)] and [Emim][BF\({}_{4}\)]), from residual sands after ILs-enhanced solvent extraction of oil sands. This process was conducted as two steps: water washing of the residual solids to remove ILs into aqueous solution; adsorption and desorption of ILs from the solution by the sulfonic acid cation-exchange resin (Amberlite IR 120 Na). Surface characterization showed that the hydrophilic ionic liquids could be completely removed from the solid surfaces by 3 times of water washing. The ionic liquids solution was treated by the ion-exchange resin. Results showed that more than 95% of [Bmim][BF\({}_{4}\)] and 90% of [Emim][BF\({}_{4}\)] could be adsorbed by the resins at 20 \({}^{\circ}\)C with contact time of 30 min. The effects of some typical coexisted chemicals and minerals, such as salinity, kaolinite (Al\({}_{4}\)[Si\({}_{4}\)O\({}_{10}\)](OH)\({}_{8}\)), and silica (SiO\({}_{2}\)), in the solution on the adsorption of ionic liquids have also been investigated. Results showed that both kaolinite and SiO\({}_{2}\) exerted a slight effect on the uptake of [Bmim][BF\({}_{4}\)]. However, it was observed that increasing the ionic strength of the solution by adding salts would deteriorate the adsorption of [Bmim]\({}^{+}\) on the resin. The adsorption behaviors of two ILs fit well with the Sips model, suggesting the heterogeneous adsorption of ionic liquids onto resin. The adsorption of ionic liquids onto Amberlite IR 120 Na resin was found to be pseudo-second-order adsorption. The regeneration tests showed stable performance of ion-exchange resins over three adsorption-desorption cycles.  
Keywords:ionic liquids; ion-exchange resin; adsorption; removal; residual sand; washing +
Footnote †: journal: Applied Physics Letters
# Real-Time Precise Point Positioning during Outages of the PPP-B2b Service
## 2 Methodologies  
With the received information transmitted via GEO satellites, the user can recover the PPP-B2b precision products in conjunction with the broadcast ephemeris according to the method provided in the official BeiDou documentation [11]. With the final orbit and clock products from geodetic benchmark (GBM) as the reference, the performances of the PPP-B2b corrections are evaluated in the following subsections.
# Energy Efficiency Improvement in Reconfigurable Photovoltaic Systems: An Evaluation of Team Systems
## 2 Sizing Ratio in GCPV Systems
### Simulation Models
#### 2.2.2 Central Inverter Model  
The available power at the inverter output (\(P_{AC}(t)\)) depends on both the inverter input power (\(P_{DC}(t)\)) and the inverter's efficiency (\(\eta_{INV}\)). There are some models in the literature to represent the inverter's efficiency curve, but the model presented in [37] is commonly used. This model is only applicable for output power ranges lower than the inverter rated power (\(P_{INV}\)) and it is given by Equation (5).  
\[\eta_{INV}=\frac{p_{ac}(t)}{k_{0}+k_{1}\cdot p_{ac}(t)+k_{2}\cdot p_{ac}{}^{2} (t)}\text{ being }p_{ac}(t)=\frac{P_{AC}(t)}{P_{INV}} \tag{5}\]  
In these expressions, \(p_{ac}(t)\) is the inverter output power normalized to the inverter rated power (\(P_{INV}\)), \(k_{0}\) stands for the losses coefficient at no load, and \(k_{1}\) and \(k_{2}\) stand for coefficients corresponding to losses varying linearly and quadratically with the inverter current.  
For output power ranges higher than the inverter nominal power, the models commonly utilized assume the limitation of the inverter output power to this nominal value, as shown in Equation (6).  
\[P_{AC}(t)=P_{INV}\ \rightarrow\ p_{ac}(t)=\frac{P_{AC}(t)}{P_{INV}}=1 \tag{6}\]
# Supplier Replacement Model in a One-Level Assembly System under Lead-Time Uncertainty
## 3 Problem Description
\[\sum_{j=0}^{u_{i}^{0}-1}y_{i}^{j}=1\quad\quad\forall i=1,...,n \tag{5}\]  
\[x_{i}^{j}\leq(u_{i}^{0}-j)\cdot y_{i}^{j}\quad\quad\forall i=1,...,n,\forall j =0,...,u_{i}^{0}-1 \tag{6}\]  
\[x_{i}^{j}\in\mathbb{N}\quad\quad\forall i=1,...,n\quad\forall j=0,...,u_{i}^{ 0}-1 \tag{7}\]  
\[y_{i}^{j}\in\{0,1\}\quad\quad\forall i=1,...,n\quad\forall j=0,...,u_{i}^{0}-1 \tag{8}\]  
The objective function in Equation (4) represents the mathematical expectation of the total cost composed of purchase cost (noted \(PC(Y)\)), backlogging cost (noted \(EBC(X,Y)\)) and holding component costs (noted \(EHC(X,Y)\)):  
\[PC(Y)=\sum_{i=1}^{n}\sum_{j=0}^{u_{i}^{0}-1}PC_{i}^{j}\cdot y_{i}^{j} \tag{9}\]  
\[EBC(X,Y)=b\cdot\sum_{k\geq 0}\left(1-\prod_{i=1}^{n}\left(\sum_{j=0}^{u_{i}^{0}-1 }y_{i}^{j}\cdot F_{i}^{j}(x_{i}^{j}+k)\right)\right) \tag{10}\]  
\[EHC(X,Y)=\sum_{i=1}^{n}\sum_{j=0}^{u_{i}^{0}-1}\left(y_{i}^{j}\cdot h_{i}\left( x_{i}^{j}-E(L_{i}^{j})\right)\right)+\sum_{i=1}^{n}(h_{i})\cdot\sum_{k\geq 0} \left(1-\prod_{i=1}^{n}\left(\sum_{j=0}^{u_{i}^{0}-1}y_{i}^{j}\cdot F_{i}^{j}( x_{i}^{j}+k)\right)\right) \tag{11}\]  
Without taking into account the APC, the backlogging cost is equal to \(b\cdot\underset{i=1,...,n}{max}(L_{i}-x_{i})\). We know that \(L_{i}\) is a positive random discrete variable with a finite number of possible values and \(F_{i}(. )\) is its cumulative distribution function. Thus, the expected value of the backlogging cost is equal to \(b\cdot\sum_{k\geq 0}\left(1-\prod_{i=1}^{n}F_{i}(x_{i}+k)\right)\) where \(F_{i}(x_{i}+k)=P(L_{i}\leq x_{i}+k)\). If we integrate purchase prices as additional decision variables, we can easily deduce \(EBC(X,Y)\) expressed in Equation (10). In the same way, we can calculate \(EHC(X,Y)\) expressed in Equation (11). Constraints in Equation (5) express the fact that only one purchasing policy \((j=0,...,u_{i}^{0}-1)\) is selected for each supplier \((i=1,...,n)\). Constraints in Equation (6) ensure that each planned lead time \(x_{i}^{j}\) is limited by an upper bound. This upper bound for the purchasing policy (\(j=0\)) is equal to \(u_{i}^{0}\). The goal is to minimize Equation (4) subject to Equations (5)-(8). This minimization is fairly difficult because the objective function is not linear and because decision variables \(X=(x_{i^{\prime}}^{j}i=1,...,n;j=0,...,u_{i}^{0}-1)\) are integer and \(Y=(y_{i}^{j};i=1,...,n;j=0,...,u_{i}^{0}-1)\) are binary. We note that the B&B algorithm proposed in [3], cannot be used for this new problem, because the bounds and properties are no longer valid. Moreover, the problem is more complex, thus we will develop an optimization approach for it based on a genetic algorithm (GA) and two bounds to verify the usefulness of our approach.
# Future Industrial Networks in Process Automation: Goals, Challenges, and Future Directions
## 3 Technology Enablers for Future Control Architecture  
Fundamental technology drivers that make the future industrial network happen are seen in the realization of the three main domains of (i) deterministic communication infrastructure, (ii) edge/fog computing, and (iii) security.  
Deterministic communication infrastructures are expected to be addressed according to the Time-Sensitive Networking (TSN) standard, which is under the development of IEEE standardization. TSN is working to provide an innovative networking solution for closed-loop control systems that support mixed traffic in critical applications such as transportation (vehicular/automotive networks, railway, and autonomous cars), motion control, power utility automation, and industrial distributed control systems [8]. Another technology driver is fog/edge computing, which extends cloud computing capabilities in terms of storage, computation, and networking to the edge of the network [9]. Fog computing is the leading architectural enabler supporting low latency, low-power consumption, high reliability, and location awareness that might be of interest in the aforementioned critical applications. Security is another crucial entity that enables future industrial automation. In traditional industrial networks, the main security concern usually correlates to safety definition, where the main goal is to protect humans and machines against the consequences of system failures [10]. By integrating information technology into the industrial control systems, protection against cyberattacks has become the major design goal of Industrial IoT systems [11].  
In this paper, we focus on the core functionality of enabling IT traffic in the OT networks. The main challenge is to find means to accommodate an unknown amount of IT traffic while preserving the real-time guarantees for OT traffic in the network. TSN is a developing technology that is expected to have a significant impact on the realization of such functionality while providing a deterministic communication infrastructure. Unfortunately, there is still a lack of corresponding technology candidates for edge/fog computing and security solutions. We believe that when IT and OT convergence is in place, the focus can shift to higher-level functionality and new ways of deploying automation systems for process automation, like edge and cloud solutions. However, history has proven that security cannot easily be retrofitted, and thus, this has to have been taken into account from the start.  
Figure 8: Traffic between the connectivity servers and operator and engineering stations in VLAN-F. The pie chart shows the proportion of consumed bandwidth by various communication protocols.
## 4 Control Analysis Based on Sensitivity
### Inaccuracies on Modelling  
Modelling inaccuracies can result in instability problems. For this purpose, the additive uncertainty, \(\Delta G(s)\), and the multiplicative uncertainty, \(\delta G(s)\), are usually considered. Thus, part of the robust stability problem can be applied:  
\[\frac{|\Delta G(s)|}{|\,G(s)|}=|\delta G(s)|<\frac{1}{|T(s)|}. \tag{22}\]  
Conducting a similar analysis as the one in Section 4.3, it is possible to define the maximum tolerable uncertainty as  
\[\text{M}_{\text{t}}=\underset{\omega}{max}|T(\text{I}\omega)|=\underset{ \omega}{max}\frac{C(\text{I}\omega)G(\text{I}\omega)}{1+C(\text{I}\omega)G( \text{I}\omega)}. \tag{23}\]  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline \(\mathbf{\alpha}\) & \(\mathbf{f_{\text{Ms}}}\) (Hz) & \(\text{M}_{\text{s}}\) (dB) & \(\text{M}_{\text{s}}\) & \(\text{\boldmath$\alpha_{s}$}\) & \(\text{GM}\) & \(\text{PM}\) (\({}^{\circ}\)) & \(\text{GM}\) \({}^{*}\) & \(\text{PM}\) \({}^{*}\) (\({}^{\circ}\)) \\ \hline PRHC & 987.7 & 3.375 & 1.475 & 0.678 & 17.67 & 40.097 & 3.11 & 39.634 \\
0.75 & 3009.9 & 2.231 & 1.293 & 0.773 & 18.96 & 54.719 & 4.41 & 45.504 \\
1 & 2798.6 & 1.979 & 1.256 & 0.796 & 19.71 & 55.997 & 4.91 & 46.922 \\
1.5 & 1965.6 & 2.071 & 1.269 & 0.788 & 18.42 & 56.247 & 4.71 & 43.99 \\ \hline \end{tabular} Superscript \({}^{*}\) denotes alternative gain and phase margin defined by (21).  
\end{table}
Table 4: Tuned stability parameters, based on controller gains from Table 3.  
Figure 9: Nyquist of \(L(\text{I}\omega)\) ending trajectory. Grey line defines the unitary circle for discrete\(-\)time systems.  
In this case, (23) concerns the complementary sensitivity function \(T(s)\) instead of \(S(s)\). For this reason, the controller plays a relevant role in the stability and robustness of the system.  
Table 5 summarises, in the system's worst case, the maximum admissible pure variation at the plant \(G(s)\) in terms of gain, \(\Delta G_{\text{Mt}}\), and in terms of phase, \(\Delta\text{P}_{\text{Mt}}\). The results show that FPRs with \(\alpha\) values above 1 are better in terms of \(\Delta G_{\text{Mt}}\) and \(\Delta\text{P}_{\text{Mt}}\) than a standard PR controller.  
Figure 10 represents the gain bode diagram's superior limit for the multiplicative (\(\Delta G\)) and additive (\(\delta G\)) uncertainties, respectively. The figure shows the upper limits of \(\Delta G\) and \(\delta G\), concluding that up to 350 Hz, all options are similar. Still, from 350 Hz, the controller with the best plant variation tolerance is the FPR with \(\alpha=1.5\), either in terms of \(\Delta G_{\text{Mt}}\) and \(\Delta\text{P}_{\text{Mt}}\), and the PRHC is the one with the worst tolerance.
# Application of ANN in Predicting the Cantilever Wall Deflection in Undrained Clay
## 2 Materials and Methods
### Architecture of the Developed ANN Model for Cantilever Wall Deflection  
To select the appropriate hidden neurons to obtain the best ANN geometry, a trial-and-error procedure was performed. First, the collected datasets were normalized in a range of \(-1\) and \(1\) based on Equation (4) to achieve dimensional consistency for all the parameters and avoid potential overfitting issues.  
\[Z_{n}=2\times\frac{(Z_{i}-Z_{\text{min}})}{(Z_{\text{max}}-Z_{\text{min}})}-1 \tag{4}\]  
where \(Z_{n}\) represents the normalized data sample; \(Z_{i}\) represents the data sample; \(Z_{\text{max}}\) and \(Z_{min}\) stand for the maximum and minimum values of the data for the interested parameter, respectively.  
Second, the collected database was randomly spill into training, testing, and validation sets, respectively. The training ratio was set as 0.70, while the testing and validation ratios were set as equal, i.e., 0.15, respectively. The training set was utilized to compute the associated weights matrix and the biases vector in Equation (2), and the testing set was used to evaluate the performance of the ANN model. Furthermore, the validation set was used to monitor ANN model overfitting. The error of validation data will increase accordingly once overfitting occurs.  
Finally, the number of neurons in the hidden layer varied from 1 to 12, and accordingly, a total of 12 ANN models were trained. Figure 6 presents the computed MSE of the training, testing, and validation sets for different numbers of neuron. It can be found that the case of 6 neurons in the hidden layer results in the best performance. The obtained best ANN architecture is schematically shown in Figure 7. It shows 6 neurons in the input layer, 6 neurons in the hidden layer and 1 neuron in the output layer.  
Figure 5: Activation functions of ANN model: (**a**) PURELIN; (**b**) and LOGSIG function.  
Figure 6: The MSE for different neuron number in the hidden layer.  
Figure 7: Architecture of the developed ANN model.
## 3 Results and Discussion
### BLUF Modules in Association with the Effector Domains
#### 3.3.1 EAL and GGDEF Domain
The query protein sequence (ARH96915.1) containing the EAL output domain was subjected to a protein-protein interaction analysis, which revealed several interacting partners involved in the regulation of different signaling pathways (Figure S7a; Table S1). The protein-protein interaction analysis showed that most of the interacting partners for this photoreceptor are either EAL domain-containingPDEs (JD73_03740) or GGDEF domain-containing DGCs (JD73_23675, JD73_25605, JD73_23680, YeaP, and YdaM). However, the interactions with YcgZ (two-component connector protein) and YcgE (Mer-like repressor protein/transcriptional regulator) have also been revealed, indicating their involvement in the regulation of bacterial biofilm formation [30]. [PERSON] et al. [30] have characterized the previously unknown function of the BLUF-EAL domain-containing protein, YcgF, from _E. coli_, and suggested that upon blue light irradiation, this protein acts like an antirepressor. The antirepressor YcgF removesYcgE (Mer-like repressor) from the promoter, and resumes the expression of different small regulatory proteins (YmgA and YmgB). These small regulatory proteins (YmgA and YmgB) utilize the RscC/RscD/RscB two-component phosphorelay system to activate the production of colanic acid, a biofilm matrix component, and to decrease adhesive curli fimbiae [30]. The query protein may also interact with AriR/YmgB (regulator of acid resistance influenced by indole), another biofilm-related protein involved in the regulation of acid resistance in _E. coli_[81]. A string analysis also revealed the possible interaction between the query protein and regulatory protein, LuxR, which regulates quorum sensing in the bacterial system [82; 83].
## 3 Result and Discussion
### Parametric Study  
Conducting a parametric study is of significance by varying a parameter alone at the fixed remaining values in order to evaluate the characteristics of the exergy destruction. Therefore, the efficiencies of compressors and turbines (\(\eta\)), pinch temperatures of heat exchangers and CS (\(\Delta\tau_{min}\)), pressure drop in TV2 (\(\Delta p_{TV2}\)), storage pressure in HST (\(p_{7}\)), compressor inlet pressure (\(p_{3}\)) and ambient temperature (\(\tau_{amb}\)) are examined individually. Figures 3-9 depict the impacts of the parameters on the results achieved from both conventional and advanced exergy analyses.
# New Flexible Protective Coating for Printed Smart Textiles
## 4 Discussion
### Improvement of the Coating  
Due to the formation of pores during the printing/drying processes, a single coating step did not effectively protect the printed conductive layer. This could also explain the relatively high water vapor permeability measured with the single-layer coatings containing LAPONITE layered silicates.  
By repeating the printing process and thus applying a thicker protective coating (70-80 \(\upmu\)m), the likelihood of pore formation was reduced. In this case, a pinhole test showed that a continuous coating was obtained on the printed conductive pattern. It can therefore be concluded that polyurethane dispersions mixed with layered silicates are suitable for the protection of printed smart textiles as long as the pore formation during the printing process is limited.  
The formation of these pores could be due to:  
* The elimination of the water present in the dispersion during the drying process, especially if the drying temperature exceeds 100 \({}^{\circ}\)C too early; or
* Air entrapment during the printing process and paste preparation.  
In addition to avoiding the formation of gas bubbles by modifying the printing and drying parameters, it is possible to promote their removal.  
According to [PERSON] law (Equation (1)), the rising speed of the bubbles depends on the dynamic viscosity of the coating, among other things.  
\[v=\tfrac{2}{9}\tfrac{(\rho_{b}-\rho_{f})}{\mu}gR^{2} \tag{1}\]  
where:  
* \(v\) the rising speed of the bubble (m/s);
* \(g\) is the gravitational field strength (m/s\({}^{2}\));
* \(R\) is the radius of the gas bubble (m);
* \(\rho_{p}\) is the mass density of the particles (kg/m\({}^{3}\));
* \(\rho_{f}\) is the mass density of the fluid (kg/m\({}^{3}\));
* \(\mu\) is the dynamic viscosity of the coating (kg/(m \(\times\) s)).  
A reduction in the solid content in the dispersion (and therefore a reduction in its viscosity) could improve the evacuation of gas bubbles and thus the barrier properties.  
The use of defoamer or de-aerator additives in the composition of the coating should also be tested to avoid the formation of pores. In this way, one could expect to obtain the same barrier properties with even thinner coatings.
# Dynamic Cooperative MAC Protocol for Navigation Carrier Ad Hoc Networks: A DiffServ-Based Approach
## 4 DiffServ-Based Dynamic Cooperative MAC Protocol
### MAC Sub-Protocol for Periodic Communication
#### 4.1.1 Adaptive TDMA Schedule  
The basic hypothesis applicable to DiffServ-based clustering topology is outlined as follows:  
**Assumption 3.** Under PC mode, only one member of a 1-hop cluster is allowed to send the packet at the same time, while the other nodes overhear the medium.  
**Assumption 4.** The time axis of the adapt-TDMA schemes is divided by means of _round_, _frame_, and _mini-slot_, with the relationships \(round=m\cdot frames\), \(frame=k\cdot minislots\).  
**Assumption 5.** The major function of a cluster-head \(C_{CH}\) is assumed to have the following features: (i) its function is to monitor, supervise, and gather the state change from its cluster members; (ii) it broadcasts a common management packet (denoted by CM) periodically, including a synchronized clock, access schedule (AS), etc.; and (iii) it is not designated as fixed relay nodes, in order to avoid the problems of \"hot spots\" and imbalance link loads.  
As depicted in Figure 2, _round_ indicates the total time for all \(m\) members of a cluster to update the data according to AS. _Frame_ is the duration for one member to update its data, and can be subdivided into two _sub-frames_: a control sub-frame (_C-frame_) and a data sub-frame (_D-frame_). _C-frame_ is the time for schedule, reservation, and contention window; _D-frame_ is the time duration to handle the packet. _Mini-slot_ is the fixed time duration to process the data less than \(l_{x}\) bits. \(C-frame=k_{1}\cdot minislots\), \(D-frame=k_{2}\cdot minislots\); herein, \(k_{1}\) and \(k_{2}\) are two known constants, which represent the lengths of the \(C-frame\) and \(D-frame\), respectively.  
Furthermore, a parity field (PF) is introduced in the head of the _C-frame_, which is a 2-bit code with settings as follows: PF = 00 (i.e., overhear or idle mode in PC), PF = 01 (i.e., active mode in PC), PF = 10 (i.e., initial or idle mode in OC), PF = 11 (i.e., active mode in OC).
# A Survey on Machine-Learning Based Security Design for Cyber-Physical Systems
## 3 Taxonomy of Cyber-Physical Attacks
### Application Layer  
The application layer provides intelligent functions to CPS users and computing performance-limited physical systems. From the input/output (I/O) interfaces in the computing system, such as the serial communication port or network interface card (NIC), the attacker can intrude into the computing system and access important computing components, including file systems, cache memory, and process schedulers, where the attacker can launch attacks to disrupt the computing system. The more sophisticated the application layer, the larger the computing system becomes, and the higher the complexity; however, due to the complexity of the computing system, it is difficult to prevent an attack that targets the system.  
Figure 5 shows two types of attack that can occur in the application layer. First, an application software attack disturbs the execution of CPS application software, resulting in the return of faulty control commands to the physical system or the generation of a system error. Second, the computing hardware attack executes malicious system commands that can damage computing hardware, such as the power supply, the CPU, and memory, and therefore the computing hardware attack disrupts the computing system itself.