esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The SDRE Method
### SDRE Control Method  
Consider the autonomous, infinite-horizon, non-linear regulator problem of minimizing the performance index \(J\)  
\[J=\frac{1}{2}\int_{0}^{\infty}(\mathbf{x}^{T}\mathbf{Q}(\mathbf{x})\mathbf{x}+\mathbf{u}^{T}\mathbf{R}( \mathbf{x})\mathbf{u})\mathrm{d}t \tag{22}\]  
with respect to the state \(\mathbf{x}\) and control \(\mathbf{u}\), subject to the nonlinear differential constraints  
\[\dot{\mathbf{x}}=\mathbf{f}(\mathbf{x})+\mathbf{B}(\mathbf{x})\mathbf{u} \tag{23}\]  
where \(\mathbf{Q}(\mathbf{x})\geq 0\) is the State-Dependent Weighting (SDW) matrix of the state vector \(\mathbf{x}\) and \(\mathbf{R}(\mathbf{x})>0\) is the SDW matrix of the input vector \(\mathbf{u}\) and \(\mathbf{f}(0)=0\). The SDRE approach for obtaining a suboptimal, locally asymptotically stabilizing solution of problem Equations (22) and (23) is:  
1. Use direct parameterization to bring the nonlinear dynamics to the state dependent coefficient (SDC) form \[\dot{\mathbf{x}}=\mathbf{A}(\mathbf{x})\mathbf{x}+\mathbf{B}(\mathbf{x})\mathbf{u},\ (\mathbf{f}(\mathbf{x})=\mathbf{A}(\mathbf{x})\mathbf{x})\] (24) where a system \(\dot{\mathbf{x}}=\mathbf{A}(\mathbf{x})\mathbf{x}+\mathbf{B}(\mathbf{x})\mathbf{u}\) is called a State-Dependent Linear Representation (SDLR). In the multivariable case, it is well known that if \(\mathbf{f}(\mathbf{x})\) is a continuously differentiable function of \(\mathbf{x}\), there is an infinite number of ways to factor \(\mathbf{f}(\mathbf{x})\) into \(\mathbf{A}(\mathbf{x})\mathbf{x}\). In order to obtain a valid solution of the SDRE, the pair \(\mathbf{A}(\mathbf{x}),\mathbf{B}(\mathbf{x})\) has to be pointwise stabilizable in the linear sense for all \(\mathbf{x}\) in the domain of interest.
2. Solve the state-dependent Riccati equation \[\mathbf{A}^{T}\mathbf{P}+\mathbf{P}\mathbf{A}-\mathbf{P}\mathbf{R}^{-1}\mathbf{B}^{T}\mathbf{P}+\mathbf{Q}=\mathbf{0}\] (25) to obtain \(\mathbf{P}(\mathbf{x})>\mathbf{0}\).
3. Construct the nonlinear feedback controller equation: \[\mathbf{u}=-\mathbf{R}(\mathbf{x})^{-1}\mathbf{B}(\mathbf{x})^{T}\mathbf{P}(\mathbf{x})\mathbf{x}\] (26)  
Figure 7: Time history of input the optimal FF control to the real model and ideal model. (**a**) Time history of satellite Euler angle; (**b**) Time history of antenna joint angle.
## Materials and Methods
### Synthesis of [EMIm]F  
The reaction principle of synthesizing [EMIm]F is based on the different solubility of silver halide. The reaction equations from [EMIm]Cl are shown in Figure 2.  
Figure 1: The generic structure of alkyl-substituted imidazolium=based ILs. R\({}_{1}\) = alkyl (methyl typically), R\({}_{2}\) = alkyl, \(X^{-}\) = anion (halide, BF\({}_{4}\)\({}^{-}\), PF\({}_{6}\)\({}^{-}\), Tf\({}_{2}\)N\({}^{-}\), typically).  
The synthesis procedure of [EMIm]F is as follows. (1) Twenty-five milliliters of 2.76 M [EMIm]Cl aqueous solution and 20 mL of 3.45 M AgF aqueous solution (mole ratio of [EMIm]Cl/AgF is 1:1) were mixed. A white precipitate was observed, indicating that the metathesis reaction began rapidly. (2) The mixture was kept for 24 h under stirring. Subsequently, the liquid was separated from the precipitate via filtration. (3) The filtrate was dried for 48 h under vacuum conditions and a viscous liquid, [EMIm]F, was obtained. (4) The product was weighed, and the yield of [EMIm]F was found to be 95-98%. (5) In order to check the impurities removal and the purity of [EMIm]F, two samples of the final product were titrated via AgNO\({}_{3}\) and NaCl, respectively. No precipitation indicated that Cl\({}^{-}\) and Ag\({}^{+}\) ions were removed completely.
# Reconstruction of Epidemiological Data in Hungary Using Stochastic Model Predictive Control
## 4 Statistical Analysis for Normally Distributed Model Parameters  
### Gaussian Assumptions  
In this section, we allowed the model parameters to vary in time (\(\hat{\theta}:k\mapsto\hat{\theta}_{k}\)), but we assumed that the parameter process \(\hat{\theta}\) is a collection of _independent identically distributed_ (i.i.d.) Gaussian random variables:  
\[\hat{\theta}_{k}\sim\mathcal{N}(u^{\theta},\Sigma^{\theta})\text{ for all }k=0,1,\dots,T-1, \tag{8}\]  
where \(u^{\theta}\) is the expected value of \(\hat{\theta}_{k}\) corresponding to the values presented in Section 2.1 and the diagonal \(\Sigma^{\theta}\) is its variance. The expected value of the parameter vector containsthe nominal values from Table 1, whereas the variances are determined such that the uncertainty intervals from Table 1 resemble the \(2\sigma\) confidence intervals. Moreover, we assumed that the initial state, from which the prediction was performed, is itself a random variable, namely:  
\[\hat{x}_{0}\sim\mathcal{N}\big{(}\mu_{0}^{x},\Sigma_{0}^{x}\big{)}. \tag{9}\]  
Consequently, every further state and output are random variables, which obey the following stochastic recursion and output equation:  
\[\hat{x}_{k+1}=f(\hat{x}_{k},\hat{u}_{k},\hat{\theta}_{k},v_{k}),\ \hat{y}_{k}=C\hat{x}_{k}. \tag{10}\]  
Due to the nonlinear terms in the state transition function \(f\), the distribution of the predicted states \(\hat{x}_{k}\) becomes more and more complicated as we look forward in time (\(k=1,2,\dots\)). Therefore, it is very inefficient to compute or at least approximate the non-Gaussian probability density functions of the predicted states for the nonlinear stochastic model (10). As is commonly done in the literature (see, e.g., [50; 51; 52; 53]), we performed a tube-like trajectory estimation. With this technique, each predicted state \(\hat{x}_{k}\) is described by the first two moments, the expected value \(\mu_{k}^{x}\), and the variance \(\Sigma_{k}^{x}\), namely the states are approximated by normal distributions:  
\[\hat{x}_{k}\sim\mathcal{N}(\mu_{k}^{x},\Sigma_{k}^{x})\ \text{for all}\ k=0,1,2,\dots \tag{11}\]
# Quantum Computing Approaches for Mission Covering Optimization
## 5 Analyses of Results  
In this section, we compare the results of the different MCO implementations. Employing the implementation methods discussed above, the MCO problem was run on the D-Wave and on IBM machines, capturing several key metrics:  
* Number of qubits,
* Quantum processor time,
* Cost,
* Number of constraints violated.  
For both scenarios (see Table 1), 100 random MCO configurations were generated using up to 27-qubits (200 different MCO configurations in total). Quantum Annealing, QAOA, QAOAH, and Brute Force (BF) methods were run for each generated configuration. For Quantum Annealing, the DW_2000Q_6 machine was used, while ibmq_toronto, ibm_hanoi, ibm_cairo, ibmq_mumbai, and ibmq_montreal machines were used for running QAOA and QAOAH. The Lagrange multipliers were set to five for both scenarios, and a \(p\)-value of two is used for QAOA (this parameter is discussed in the original paper [17]). The Quantum Annealing runs each sample of the anneal 50 times, while each IBM job sampled the state-vector 1000 times. These parameters were chosen based on a good balance of timing, cost, and constraints satisfied found by preliminary results not discussed in this paper.
## 3 Results and Discussion
### Mechanical Strength  
The results of compressive strength measurements are presented in Figure 6, showing that the mechanical strength of the obtained products is reaching maximum values after the application of thermal treatment at 1300 \({}^{\circ}\)C, with the optimum results shown for the W8 sample; the respective values under these conditions are greater than 120 MPa. The results also reveal that the hematite addition improves the mechanical strength with the optimum results found after the addition of 5 wt.% Fe\({}_{2}\)O\({}_{3}\) for all raw samples (i.e., those directly collected from the mine area), as well as after thermal treatment at 1300 \({}^{\circ}\)C for 120 min. However, the sample W5 PT, due to its different mineralogical content (containing almost 100% olivine) shows a slightly better mechanical strength even without the addition of hematite. Furthermore, it is noteworthy that the thermal pre-treatment (at 850 \({}^{\circ}\)C for 30 min) of the examined samples does not favor significantly the mechanical strength values due to the higher presence of free silicon in these samples. Firing at temperatures higher than 1300 \({}^{\circ}\)C leads to the higher presence/formation of glassy phases, which reduce the bonding strength between the grain boundaries, resulting in lower mechanical strength and, hence, the respective product is not suitable as a refractory material, noting that the relevant limit is usually around 22 MPa [20].
## 5 Performance Analysis
### Comparisons for Multiple Antenna Arrays DM and Single Antenna Array DM Schemes  
The comparisons for multiple antenna arrays DM and single antenna array DM schemes [7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27] are given in Table 1, from which the main features of the proposed DM schemes can be summarized as follows:  
(1) Because the AN is inserted into the transmitting signals, both the pre-coding vector for the confidential information and the orthogonal projection matrix for the AN are required for all AN-aided DM schemes.  
(2) Although multiple transmit antenna arrays are utilized in the proposed DM schemes, the computation complexity at the transmitter side doesn't increase when transmitting \(K\) symbols each time. Obviously, the cost of the BS for the proposed schemes is more than that of the traditional single antenna array DM schemes.  
(3) The proposed DM schemes can achieve precise transmission, i.e., projecting the confidential information to the LU located at the designated location. More importantly, transmission security of the proposed schemes can also be guaranteed even if the Eve is located close to the LU or the same direction as the LU.  
\begin{table}
\begin{tabular}{l l l l} \hline
**Items** & **Single-Carrier DM** & **Multi-Carrier DM** & **Single Array DM**[7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27] \\ \hline Multiple antenna arrays & Yes & Yes & No \\ Multiple carriers & No & Yes & No \\ Artificial noise & Yes & Yes & Yes \\ Precoding vector & Yes & Yes & Yes \\ Orthogonal matrix & Yes & Yes & Yes \\ Receiver complexity 1  & \(O(\xi_{1})\) & \(O(2\xi_{2})\) & \(O(2\xi_{2})\) \\ Transmitter complexity & \(O(\xi_{2})\) & \(O(\xi_{2})\) & \(O(\xi_{2})\) \\ Cost & High & Higher & Low \\ Precise transmission 2  & Yes & Yes & No \\ Neighbor security 3  & Yes & No \\ \hline \end{tabular}
\end{table}
Table 1: Comparisons for multiple antenna arrays DM and single antenna array DM schemes.  
As for the differences between the two proposed DM schemes, on the one hand, at the receiver side, the computation complexity of the single-carrier DM scheme is much higher than that of the multi-carrier DM scheme. On the other hand, the cost of the receiver for the proposed single-carrier DM scheme is much lower. Because the receive multi-beamforming method is adopted to recover the useful signal in the single-carrier DM scheme. In the multi-carrier DM scheme, the useful signals are obtained by using different band-pass filters. Therefore, the compromise of the computation complexity and the cost should be considered when choosing between these two different schemes to perform secure and precise wireless transmission.
## 3 Deep Feature-Based Adaptive Level Set Model (DFALS)
### Constructing Adaptive Time-Step  
It is worth noting that the confidence value \(\hat{y}\) also contains the positional information of the corresponding pixel. \(\hat{y}\) is relatively large if the pixel locates closely to the auroral oval region or even in it. Otherwise, \(\hat{y}\) is small when the pixel is far away from the auroral oval region.  
As shown in Figure 4c, the segmentation failure is brought by the shape energy term of LIDLSM. More specifically, when the inner and outer level set curves cannot evolve to the adjacent region of the auroral oval simultaneously, the one far from the boundary attracts the other to continue evolving and overstepping the region of the auroral oval, which results in the segmentation failure eventually. The adaptive time step \(\Delta t\) is proposed to address this problem. In this solution, the time step is large when the confidence value is small. Otherwise, the time step will be set with a low value or even zero.  
According to these requirements, the adaptive time step can be designed as follows:  
\[\Delta t=\begin{cases}max(0\,\ 4\hat{y}^{2}-8\hat{y}+3),&k\leq 300\\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ 0.5,&k>300\end{cases} \tag{8}\]  
where \(k\) refers to the \(k^{th}\) iteration of the level set evolution. Auroral oval image and the corresponding time step map in the former 300 iterations are shown in Figure 7.  
As shown by Figure 7, under the effect of the adaptive time step, the level set curve will slow down or even stop moving when approaching the nearby region of the auroral oval boundaries. Therefore, the adaptive time step can effectively prevent the contour curve from overstepping the auroral oval region and resulting in the segmentation failure. When \(k>300\), the dual level set curves continue advancing with a relatively small and stable time step \(\Delta t=0.5\).
## 5 Conclusions  
The g3 with 4% NOVEC(tm 4710/96% CO\({}_{2}\) used in this paper has a GWP of 1.6% of that of SF\({}_{6}\) and has a much shorter lifetime than that of SF\({}_{6}\). Therefore, the greenhouse effect can be significantly reduced by replacing SF\({}_{6}\) with g3. The liquefaction temperature of this mixture ratio is about \(-30\)\({}^{\circ}\)C, which meets the required minimum operating temperature for outdoor switchgear. By investigating PD characteristics in g3 and SF\({}_{6}\), it was shown that PDIVs of g3 were 76%-84% of that of SF\({}_{6}\), depending on the type of electrode system and gas pressure. Compared with SF\({}_{6}\), PD pulses in g3 gas had a longer rising time and pulse width, and relative amplitudes of FFT in g3 gas were much greater. Even at a lower applied voltage, the average apparent charge and pulse count in g3 were higher. From the perspective of PD, the use of g3 as a substitute for SF\({}_{6}\) requires an increase in gas pressure or a mixing ratio of NOVEC(tm 4710.  
Figure 6: Average apparent charge and pulse count in SF\({}_{6}\) and g3 in the POC.  
Figure 7: Phase-resolved partial discharge (PRPD) patterns in the POC. (**a**) In SF\({}_{6}\); (**b**) In g3.  
g3 gas mixtures are promising and emerging eco-friendly alternative insulation gases to SF6, but investigations still need to be conducted before final industrial application. Results from this study are expected to be used as fundamental material for manufacturers to develop SF6-free gas-insulated power apparatus. For future studies, much effort should be made to investigate the most appropriate compromise between insulation performance, liquefaction temperature, and environmental concerns.  
Conceptualization, G.W. and G.-S.K.; Data curation, W.-H.K. and S.-W.K.; Formal analysis, S.-W.K. and J.-R.J.; Methodology, G.-S.K. and J.-R.J.; Software, G.W. and W.-H.K.; Supervision, G.-S.K. and J.-R.J.; Writing--original draft, G.W.; Writing--review & editing, S.-W.K.  
This research received no external funding.  
The authors acknowledge Hyosung Corporation for providing the insulation gases.  
The authors declare no conflict of interest.
## 2 Observation Systems and their Integration
### Citizen-Based Geo-Sensor Networks
They establish the connection to a sensor and are able to communicate with it by using the sensor protocol definition of the SID. Similar to remote and in-situ sensors, the integration of these VGI sources with spatial data infrastructures poses new research challenges: to discover and retrieve this VGI we have to deal with the different interfaces of each Web 2.0 service (Flickr, Twitter, Facebook, _etc._) and its heterogeneous capacities. To address these issues we propose a scalable solution, which aims at improving the interoperability of the heterogeneous nature of the multiple Web 2.0 services available.
## 2 Relevant Theoretical Models
### Transformer Feature Fusion
#### 2.3.2 Cross-Attention Mechanism
The multi-head attention mechanism is computed multiple times to capture diverse feature representations. The resulting attention outputs from different heads are then superimposed to form a unified representation. \(A\) linear transformation is applied to both the superimposed feature representation \(A_{T}\) and the original feature representation \(A_{T}\). This process yields the final cross-attention feature representation \(A_{\beta}\), which maps frequency-domain features to the time-domain sequence. \[A_{T}=Concat\Big{(}Cross-Attention\Big{(}F_{f},F_{t}\Big{)}\Big{)} \tag{19}\]  
\[A_{ft}=Linear(A_{T}) \tag{20}\]The resulting feature representation is \(F\) when the frequency-domain features are added and combined with the cross-attention mechanism's time-domain sequence feature representation and the time-domain signal attention feature \(F_{t}\). On this basis, the attention mechanism is applied to calculate the attention. Through the extraction and fusion of the self-attention mechanism and the cross-attention mechanism, \(H\) is the last representation provided to the adaptive layer. \[ \begin{cases}F=A_{ft}+F_{t}\\ M=tanh(W_{m}\cdot F+b_{m})\end{cases}  \tag{21}\]  
\[\alpha=softmax(W^{T}\cdot M) \tag{22}\]  
\[H=F\cdot\alpha^{T} \tag{23}\]  
where \(W_{m}\) denotes the weight matrix corresponding to the attention mechanism layer depth, \(W_{T}\) is the initialization parameter random matrix, \(b_{m}\) is the bias vector, and \(\alpha\) is the input assignment weight.
# Environment-Aware Adaptive Reinforcement Learning-Based Routing for Vehicular Ad Hoc Networks
## 3 Preliminaries
### Motivation Scenario
The proposed protocol is envisioned as a means to relay data in distributed information applications and services that require multihop communication within vehicular networks. The goal is to enhance data delivery rates and reduce network congestion. The design model we have proposed leverages the mobility of vehicles to collect data along road segments, thereby minimizing the costs associated with complex infrastructure. It simultaneously ensures the real-time and accurate diffusion of traffic information.
# Anonymous Access System with Limited Number of Uses in a Trustless Environment
## 4 The Proposal
### Credential Issuance Protocol  
The credential issuance protocol comprises Steps 1, 2 and 3 of Figure 1 and is analogous to the credential issuance protocol proposed in [4]. The details of this protocol are described below.  
1. \(\mathcal{U}\) sends the scope \(S\) and their public key \(pk_{\mathcal{U}}\) to \(\mathcal{AP}\).
2. \(\mathcal{AP}\) computes the verifiable attribute \(\delta_{\mathcal{AP}}\) as follows: \[\delta_{\mathcal{AP}}=(H(S)+sk_{\mathcal{AP}})^{-1}\cdot pk_{\mathcal{U}}\]  
Figure 1: Overview of the protocol interactions.  
that links the scope \(S\) with \(\mathcal{U}\)'s public key through the private key of \(\mathcal{AP}\), and sends \(\delta_{\mathcal{AP}}\) back to \(\mathcal{U}\).
3. \(\mathcal{U}\) stores the computed \(\delta_{\mathcal{AP}}\) received from \(\mathcal{AP}\) and computes and stores \(id_{\mathcal{U}}\): \[id_{\mathcal{U}}=H(S)^{-1}(sk_{\mathcal{U}}+H(S))^{-1}\cdot pk_{\mathcal{U}}\] (10)
## 2 Computational Model
### Model Validation  
A high fidelity engine model validated against experimental data for a conventional layout, in both steady-state and transients, has been applied in this study. The calibration of the combustion model (SI Wiebe) was performed by isolating Cylinder 1 of the engine and performing a Three Pressure Analysis (TPA). Figure 4 represents the comparison between measurements and simulation results for all engine cylinders at low loads.  
The small cylinder-to-cylinder variations observed in the measured data were difficult to capture in the 1D simulation. These variations may be a result of several factors, such as 3D air and gas flow behaviours at the intake and exhaust or different thermal conditions. Although there were slight variations, a good agreement between measured and simulated mass air flow and averaged indicated mean effective pressure (IMEP) (error of less than 2%) was observed for full load conditions, as shown in Table 2.  
For part-load cases, the discrepancy between measured and simulated results was slightly larger, as can be found in Table 3. However, despite the 9.37% error in the averaged IMEP for the low-load case, the averaged absolute error in net IMEP was below 0.3 bar. Therefore, the modelling of the scavenging system and combustion system at low engine load is satisfactory.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Attribute Value** & **Cylinder 1** & **Cylinder 2** & **Cylinder 3** & **Cylinder 4** \\ \hline Measured total mass air flow (kg/h) & & & 258.69 & \\ Simulated total mass air flow (kg/h) & & & 263.65 & \\ Error in total mass air flow (\%) & & & 1.92 & \\ Measured net IMEP (bar) & 20.71 & 21.19 & 20.75 & 18.29 \\ Simulated net IMEP (bar) & 20.44 & 20.76 & 20.30 & 18.02 \\ Absolute error in net IMEP prediction (bar) & 0.39 & 0.43 & 0.45 & 0.27 \\ Error in net IMEP prediction (\%) & 1.87 & 2.05 & 2.15 & 1.47 \\ Averaged absolute error in net IMEP (bar) & & & 0.36 & \\ Averaged error in net IMEP (\%) & & & 1.89 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Measured data and simulation results at full loads. IMEP, indicated mean effective pressure.  
Figure 4: Simulated cylinder pressure from Three Pressure Analysis (TPA) and measured pressure at low loads. (**a**) Cylinder 1; (**b**) Cylinder 2; (**c**) Cylinder 3; (**d**) Cylinder 4.
## 1 Introduction
However, consolidated research covering all the issues does not appear to have occurred yet. A previous milestone survey [16] focused on briefly describing the contribution of the relevant DCS techniques. The survey classified the DCS schemes according to multi-replication, storage policies and routing. The survey, however, did not cover the requirements and challenges involved in data storage and retrieval methods such as similarity search, data aggregation, range query, multi-replication, non-uniformity of the network, load balancing and so on, rather it briefly depicted different DCS schemes. Hence, this classification may be altered to present the DCS approaches according to the problems dealt with. In this paper we provide a thematic classification that presents DCS schemes in a different light. Rather than classifying the DCS schemes according to specific techniques, a classification is proposed according to the problems the DCS schemes attempt to solve, thus providing a more efficient understanding of the proposed solutions. Furthermore, beyond an orthodox classification approach, this paper categorizes the routing algorithms used in the DCS schemes into two types of generalized routing that are referred to as point-to-point routing and spanning tree based routing. The rest of the paper is organized as follows: Section 2 briefly discusses a few challenges and the design drivers in the DCS research field. Different DCS approaches in the current state-of-the-art are briefly described and analyzed in Section 3 while Section 4 presents the classification of the DCS schemes based on the challenges illustrated in Section 2 including range query, similarity search, data aggregation, sensor network field non-uniformity, multi-replication, load balancing and routing algorithm. Finally, Section 5 draws conclusions. Throughout the paper cost metrics is considered in terms of energy, and it is measured as the number of MAC-layer hops.