esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 GHG Emissions and Production Costs of Cellulosic Ethanol
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Direct Emissions from RED Recast,** & **Indirect Emissions** & **Indirect Emissions** \\
**Annex V—Default Values [FOOTNOTE:]Footnote : footnotemark: [ENDFOOTNOTE] & **(ILUC Emissions from** & **(Displacement Emissions** \\
**(g CO\({}_{2}\)eq/MJ)** & **[PERSON] et al., 2015 [31])** & **from ICCT, 2017 [29]** \\  & & **(g CO\({}_{2}\)eq/MJ)** & **(g CO\({}_{2}\)eq/MJ)** \\ \hline  & & 16 & \\ Ethanol from wheat straw & 16 & if unsustainable straw removal & 8 \\  & & is assumed (more than 33–50\%) & displaced uses: livestock bedding \\  & & 0 & and feed; mushroom cultivation; \\  & & if sustainable straw removal & horticulture; heat and power \\  & & is assumed (less than 33–50\%) & \\ \hline Ethanol from perennial grass & & & \\ (miscanthus and swithgrass) & & & no existing uses \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Estimated greenhouse gas (GHG) emissions (direct and indirect) associated with cellulosic ethanol production from cereal straw and perennial crops. Cost of enzymes also represents a relevant cost component of the operational costs [15; 37; 40] and reducing this cost is key to making cellulosic ethanol economically viable [40]. Comparing the costs of three approaches for producing enzymes (off-site, on-site, and integrated), [PERSON] [40] found that the integrated method has the lowest cost. Generally, the reported ranges of production costs for cellulosic ethanol appear to be substantially higher compared to conventional ethanol prices and far from being competitive with fossil fuel [37].
# Intelligent Dynamic Power Control with Cell Range Expansion for Small-Cells in 5G HetNets
## 4 Simulation Results
DPC) with \(\eta\) = 8 dB are 64% and 227% higher than those of the REP schemes with \(\eta\) = 8 dB when \(\Gamma_{sth}\) = \(-20\) dB and 0 dB, respectively. Figure 11 shows the outage probability of MUEs as \(\Gamma_{sth}\) increases. The results of the conventional scheme are the worst performance due to the significant interference from neighboring SBSs. The results of the proposed DPC scheme are the same as those of the REP scheme since MUEs have no effect on \(\Gamma_{sth}\). As a result, the results of the proposed DPC and REP schemes are reduced by 123% and 191% compared to those of the conventional scheme when \(\eta\) = 4 dB and 8 dB, respectively. Figure 12 shows the outage probability of SUEs (including CUEs) as \(\Gamma_{sth}\) increases. The results of the conventional scheme are the lowest performance because SBSs have no CUEs with low SINRs. The results of the REP scheme are almost the same regardless of \(\Gamma_{sth}\) and higher than those of the conventional scheme. The results of the proposed DPC scheme decrease as \(\Gamma_{sth}\) increases. The results of the proposed DPC scheme with DPC (Prop. DPC) are higher than those of the REP scheme when \(\Gamma_{sth}<-10\) dB because the number of  
Figure 9: Mean SUE capacity in the inner zone vs. \(\Gamma_{sth}\). SUEs and CUEs in the outer zone is high. On the other hand, the results of the proposed DPC scheme (Prop. DPC) are lower than those of the REP scheme when \(\Gamma_{sth}\geq-10\) dB. In addition, the results of the proposed DPC scheme (Prop. DPC) with \(\eta=4\) dB are close to those of the conventional scheme when \(\Gamma_{sth}\geq-10\) dB since SBSs allocate the appropriate transmission power for SUEs and CUEs. As a result, the results of the proposed DPC scheme are reduced by 66% and 32% compared to those of the REP scheme when \(\eta=4\) dB and 8 dB, respectively.
# A Broadband Active Microwave Monolithically Integrated Circuit Balun in Graphene Technology
## 5 Conclusions  
A graphene-based broadband balun monolithically integrated circuit working up to 6 GHz has been demonstrated for the first time. Our integrated balun is based on SiC graphene bilayer technology and contains four individual transistors. The graphene-based balun performances are compared to state of the art baluns on semiconducting technologies having similar RF-characteristics. This comparison accounts for frequency bandwidth, phase difference, amplitude balance, and die area. Comparable performances are achieved except for the gain. The circuit performances are intrinsically correlated to the transistor performances: for the graphene-based topology, the circuit works up to one-third of the transistor's transit frequency which is suitable for the new 5G NR sub-6 standard.  
The balun concept based on the ambipolar behavior of the GFET that has been presented in this paper may be replicated on other promising materials such as TMDs materials, carbon nanotube FETs or other ambipolar FETs.  
Investigation, D.F., V.P., W.W., S.B.S., W.S., M.C.L., T.Z., E.P., H.H. and S.F.; Methodology, D.Z.; Writing--S.F, E.P original draft, all authors contributed to the final version of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Horizon 2020 Framework Program: Core2 785219.  
The authors acknowledge funding through the EU Horizon2020 research and innovation program (Graphene Flagship--Graphene Core2 785219). This work was partially supported by the French RENATECH network.  
The authors declare no conflict of interest.
## 3 Results and Discussion
The EIS Nyquist plots are presented in Figure 7 for the CS rebars in FA pore solution containing 0.6 M NaCl. The proposed electrical equivalent circuit (EEC) used to fit the EIS data is shown in Figure 8. This EEC is commonly used to model the electrical response of steel reinforcements in concrete as well as concrete pore solution, it presents two-time constants, the first one is associated with the passive film formed at high pH (at high frequencies), whereas the second one refers to the double layer (at low frequencies). This EEC proposal is supported by observing the Bode plots in Figure 9. The two changes found in the slope of the phase angle indicate the presence of two different relaxation processes [51; 52]. The EEC presents a hierarchically distributed equivalent circuit consisting of: \(R_{\mathrm{s}}\), the resistance of the solution; this is attributed to the ohmic resistance between the WE and the RE, a constant phase element (\(CPE_{\mathrm{film}}\)) associated with the passive film (high frequencies), \(R_{\mathrm{film}}\), which is the resistance of the passive film, \(CPE_{\mathrm{dl}}\) which is the constant phase element related to the double layer (low frequencies) and \(R_{\mathrm{ct}}\) which is the charge transfer resistance (presenting an inverse proportionality with the corrosion rate). The two \(R//CPE\) time constants are seen in the two changes observed in the \(-\theta\) phase angle slope from the Bode plots, indicating the existence of two different relaxation processes (see Figure 9) [53]. In both time constants, the passive film and double layer, the \(CPE\) elements representing a branched ladder \(RC\) network are presented instead of an ideal capacitor due to the non-ideal capacitance of an electrode in an active state [50]. The impedance of this \(CPE\) can be obtained by \(Z_{\mathrm{CPE}}=(Y)^{-1}(i\omega)^{-n}\), where \(Y\) is the admittance (S cm\({}^{-2}\)\(s^{n}\)), \(\omega\) is the angular frequency (rad s\({}^{-1}\)), j is the imaginary number, j\({}^{2}\) = (\(-1\)), and \(n\) is a dimensionless fraction exponent ranging from \(-1\) to \(1\). The fitting parameters of the Nyquist plots using the proposed EEC are presented in Table 3. The goodness of fit (\(\chi^{2}\)) for all the alloys tested falls within \(10^{-4}\), and the error percentage for each parameter is always below 10% for all the tests performed. Figure 6: Kramers-Kronig (KK) transforms of Annealed CS rebar exposed to FA pore solution containing 0.6 M NaCl. Figure 8: Proposed electrical equivalent circuit (EEC) to fit the EIS data. Figure 7: Nyquist plots of the thermomechanical treated CS rebars in FA pore solution containing 0.6. M NaCl: (**a**) slow cooling rate, and (**b**) fast cooling rate. Figure 9: Bode plots of the thermomechanical treated CS rebars in FA pore solution containing 0.6 M NaCl: (**a**) slow cooling rate, and (**b**) fast cooling rate.
# Evaluation of Different Control Algorithms for Carbon Dioxide Removal with Membrane Oxygenators
## 2 Modeling and Control
### Control
#### 2.2.2 Non-Linear Model Predictive Controller  
The non-linear model predictive controller (Figure 1d)) [23] is a proactive control scheme that uses a non-linear model of the system of the form:  
\[x^{+}=f(x,u) \tag{5}\]  
The idea is to predict and optimize a finite horizon trajectory for the future inputs--\(u\)--and states--\(x\)--of the system. Given the inputs and the current state, the future of the system can be calculated with the state equations (Appendix A Table A1. Equations (A1)-(A10)). However, the optimal inputs that guide the system into the desired state are not known in advance and have to be optimized for. To do that, the NMPC minimizes a quadratic cost function in the inputs and outputs under specified constraints, i.e.,  
\[\min_{u(0),\ldots,u(N-1)}J(x,u)=\sum_{k=1}^{N-1}l(x_{u}(k),u(k)) \tag{6}\]  
with  
\[l(x_{u}(k),u(k))=\|x_{u}(k)\|^{2}+\lambda\|u(k)\|^{2} \tag{7}\]  
The quadratic cost function (Equation (7)) not only penalizes deviation from the desired states but also imposes a penalty on the control action. The penalty ratio is controlled by the parameter \(\lambda\geq 0\). For \(\lambda<1\), deviation from the desired state is penalized more than control action and vice versa for \(\lambda>1\). For \(\lambda=0\), there is no penalty on the control action. Penalization of the control action has two advantages. First, imposing constraints on the possible \(u\) values make a solution easier to find numerically. Second, over-actuation, i.e., high \(u\) values, is associated with high energy consumption and even violating the physical constraints of the actuators. The NMPC minimizes the cost function over a finite time horizon, which is often different for the prediction and control sequences.  
After acquiring the optimal control sequence over the control horizon, the first element is applied to the system, and the optimization is repeated for each time step.
## 3 Rolling Bearing Remaining Useful Life (RUL) Prediction Method
### Health Indicator Evaluation  
The degradation trend of rolling bearings exhibits time variance and randomness. An effective health indicator should possess correlation (Corr), monotonicity (Mon), and robustness (Rob) [18]. In this paper, features are extracted from both the time and frequency domains of bearing vibration signals. Multi-dimensional weight information is utilized for feature fusion to construct the health indicator, effectively addressing the challenges posed by nonlinear time-series characteristics and information loss. The definitions of correlation (Corr), monotonicity (Mon), and robustness (Rob) are as follows:  
\[Corr(F,T)=\frac{\left|\sum\limits_{K=1}^{K}\left[f_{T}(t_{k})-\overline{F} \right]\left(t_{k}-\overline{T}\right)\right|}{\sqrt{\sum\limits_{K=1}^{K} \left[f_{T}(t_{k})-\overline{F}\right]^{2}\sum\limits_{K=1}^{K}\left(t_{k}- \overline{T}\right)^{2}}} \tag{11}\]  
\[Mon(F)=\left|\frac{dF>0}{K-1}-\frac{dF<0}{K-1}\right| \tag{12}\]  
\[Rob(F)=\frac{1}{K}\sum\limits_{K=1}^{K}\exp\left[-\left|\frac{f_{R}(K)}{f(K)} \right|\right] \tag{13}\]  
where \(\overline{F}\) denotes the mean value of feature sequences \(f_{T}(t_{k})\) from \(1\) to \(k\); \(\overline{T}\) denotes the mean value of time sequences \(t_{k}\) from \(1\) to \(k\); and \(dF\) denotes the derivative of the feature sequence with respect to the time sequence. The metrics Corr, Mon, and Rob are defined on the closed interval [0, 1]. Increasing values of these metrics indicate enhanced predictive performance.  
Figure 5: Overall methodology framework diagram.  
By computing various statistical features of the signal across different domains--namely, the time domain (e.g., mean, variance, kurtosis), the frequency domain (e.g., spectral centroid, spectral entropy), and the time-frequency domain (e.g., wavelet packet energy, time-frequency aggregation)--and selecting features with a Cori value greater than 0.5 [19], we construct a feature set that exhibits improved monotonicity and trend characteristics in characterizing signal variation patterns. The selected features include peak value, variance, waveform factor, kurtosis, RMS value, and root amplitude. This feature set is illustrated in Figure 6.
# Design and Evaluation of Steganographic Channels in Fifth-Generation New Radio +
## 4 Protocol Analysis and Covert Channel Design
### Summary of Pattern Analysis  
Concluding the analysis, the Packet Data Convergence Protocol (PDCP) in 5G New Radio is susceptible to both timing and storage patterns. Both types of patterns can be used to create a covert channel within the PDCP layer. However, only about half of the covert storage patterns that modify a protocol-specific field are applicable to the PDCP. Finally, only the reserved/unused pattern is considered suitable for a covert channel. All other applicable storage patterns most likely affect the functionality of the PDCP in some way and thus increase detectability. Due to the increased requirements of 5G, connection losses can occur very quickly if the timing behavior is manipulated. Therefore, covert timing patterns are generally not as suitable as covert storage patterns for information hiding on the air interface.
## 4 Results
### Comparative Analysis of Multimodal Fusion Models  
The comprehensive comparison in Table 3 highlights three key findings:  
* Dynamic Fusion Outperforms Static Strategies: CLIP-BCA-Gated (91.77% accuracy) surpasses static fusion models (e.g., ALIGN-Concat-Aug: 89.91%) by 1.86 percentage points, confirming that bidirectional cross-attention enables finer-grained text-image alignment. For example, in tweets combining \"collapsed bridge\" text with oblique-angle damage images, the BCA module aligns \"collapsed\" to structural deformation regions, whereas static fusion relies on global feature similarity.
* Adaptive Gating Enhances Noise Resilience: The model outperforms text-augmented baselines (e.g., CLIP-Txt-Aug: 83.03%) by 8.74 percentage points, demonstrating that adaptive gating effectively suppresses noisy modalities. When text contains typos (e.g., \"load\" for \"flood\"), the gating mechanism reduces text weight (\(\alpha\) = 0.31), prioritizing visual cues (waterlogged areas).
* Superiority Over State-of-the-Art Models: Compared to advanced attention models (e.g., CBAN-Dot: 88.40% F1), CLIP-BCA-Gated improves F1 by 3.34 percentage points. This advantage is attributed to the synergy of bidirectional attention and dynamic modality weighting, which captures complex cross-modal relationships.
# A Universal Low-Complexity Demapping Algorithm for Non-Uniform Constellations
## 4 Proposed Low-Complexity Demapping Algorithm
### LLR Calculation  
This section takes 2D-NUC as an example to illustrate the LLR calculation process of the SCSR algorithm, as described in Algorithm 2.  
```
1:\(y\): received symbols after equalization; \(h\): channel fading coefficient; \(N_{0}\): noise variance; \(\overline{x}\): new constellation (with modulation order \(M\)); CR: code rate
2:Output: LLR calculation result
3:for\(n=1\) to size(\(y\)) do
4: determine the quadrant \(Q\) of \(y_{n}\)
5: select the corresponding subsets \(C\) and \(C_{i}^{k}\) according to \(Q\) and CR
6:for\(m=1\) to size(\(C\)) do
7: calculate the ED between \(y_{n}\) and the \(m\)-th constellation point in \(C\)
8:endfor
9:for\(i=0\) to \(\log_{2}M\)-1 do
10: compare the EDs between \(y_{n}\) and the constellation points in \(C_{i}^{0}\) and \(C_{i}^{1}\) respectively, and obtain the minimum EDs
11: calculate LLR(\(b_{i}\))
12:endfor
```  
**Algorithm 2** LLR Calculation Process of SCSR Algorithm  
The 1D-NUC demapping process is similar to Algorithm 2, except that the I component and Q component of the received symbol need to be decomposed first, and the subsets are selected according to the sign of the I/Q component.  
It can be seen from Section 4.1 that the subsets of the SCSR algorithm are not obtained by statistical results, but are obtained by removing the constellation points that never have the minimum ED with the received symbol from \(\chi_{i}^{k}\). Therefore, if the condensation characteristic is not used, the SCSR algorithm can achieve the same performance as the Max-Log-MAP algorithm. In fact, the condensation characteristic is used, so the SCSR algorithm has a performance loss compared with the Max-Log-MAP algorithm, but its complexity is greatly reduced. The complexity and BER performance of the SCSR algorithm will be analyzed in detail in the next section.
# Experimental Validation of Ellipsoidal Techniques for State Estimation in Marine Applications
## 4 Modeling of Uncertainty
### Uncertainty in the Dynamic System Model: Bounded Process Noise
\begin{array}{ccc}\sigma_{x_{1}}^{2}&\ldots&\sigma_ {x_{4}}^{2}\end{array} \right]\}\enspace. \tag{60}\]  
**Remark 1**. : _In Section 5, the heading dependence of the system matrix \(\boldsymbol{\Phi}(\mathbf{p})\) in (55) is taken into account in two alternative forms. Firstly, these dependencies are treated as a pure state dependence, where corresponding outer bounds are determined by the proposed estimation algorithm. Second, the set-based heading identification according to the following subsection is taken into account additionally to intersect the estimated bounds with the measurement-based tolerances. This intersection enhances the knowledge on the system dynamics and hence reduces the uncertainty included in the set-valued bounds on the system matrix \(\boldsymbol{\Phi}(\mathbf{p})\)._  
**Remark 2**. : _If an a posteriori (observer-based) identification of the disturbance terms \(z_{1}(t)\) and \(z_{2}(t)\) were not possible, their influence would have to be described by means of conservative worst-case bounds. They have to be included in the terms (58) by inflating the corresponding standard deviations \(\sigma_{x_{1}}\)._
## 3 Problem Statement and Model Construction  
This section provides a detailed introduction to the concurrent emergency response problem and its mathematical model. In particular, the assumptions that help abstract the joint scheduling problem under the CER situation into a mathematical model are presented in Section 3.1. A list of the notations used throughout the study is given in Section 3.2, and Section 3.3 offers a detailed introduction to the construction of the joint scheduling model.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Type** & **Model** & **Optimization Objectives** & \
\begin{tabular}{c} **Solution** \\ **Algorithm** \\ \end{tabular} &
\begin{tabular}{c} **Competition** \\ **Consideration** \\ \end{tabular} \\ \hline
[6] & SER & MDP & \
\begin{tabular}{c} Transportation time, Road \\ network influence \\ \end{tabular} &
\begin{tabular}{c} Reinforcement \\ learning \\ \end{tabular} & – \\
[9] & SER & \
\begin{tabular}{c} Multi-objective \\ non-linear integral \\ planning model \\ \end{tabular} &
\begin{tabular}{c} Rescue time, \\ Rescue cost \\ \end{tabular} & Genetic algorithm & – \\
[4] & CER & MILP & \
\begin{tabular}{c} Transportation time, \\ Resource cost \\ \end{tabular} &
\begin{tabular}{c} Differential \\ evolution \\ \end{tabular} & – \\
[10] & CER & \
\begin{tabular}{c} Optimization \\ model \\ \end{tabular} &
\begin{tabular}{c} Response delay \\ \end{tabular} & Genetic algorithm & – \\
[11] & CER & MILP & Resource shortage &
\begin{tabular}{c} Particle Swarm \\ Optimization \\ \end{tabular} & – \\
[20] & CER & MILP & \
\begin{tabular}{c} Satisfaction of the victims, \\ Resource cost, Fairness \\ of distribution \\ \end{tabular} &
\begin{tabular}{c} Bee colony \\ optimization \\ \end{tabular} & \(\surd\) \\
[22] & CER & MILP & \
\begin{tabular}{c} Relative deprivation cost, \\ Transportation cost \\ \end{tabular} &
\begin{tabular}{c} Ant colony \\ optimization \\ \end{tabular} & \(\surd\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of research on emergency vehicle scheduling.
# Solanesol Biosynthesis in Plants
## 3 Biosynthetic Pathway of Solanesol
### Polymerization of C5 Units  
The IPP and DMAPP C5 units are catalysed by geranyl diphosphate synthase (GPS), farnesyl diphosphate synthase (FPS), and geranyl geranyl diphosphate synthase (GGPPS) to form GPP, FPP, and GGPP, respectively (Figure 2), which are the precursors of monoterpenes, sesquiterpenenes, and diterpenes, respectively [19]. GPS catalyzes the reaction in which one IPP molecule and one DMAPP molecule form a GPP (C10) molecule, while FPS catalyzes the formation of each FPP (C15) molecule from two IPP molecules and one DMAPP molecule through a two-step condensation reaction [20]. GGPPS catalyzes the formation of GGPP (C20) from three IPP molecules and one DMAPP molecule [20]. Solanesyl diphosphate synthase (SPS) catalyzes the formation of solanesyl diphosphate (SPP) by IPP, DMAPP, GPP, FPP, and GGPP (Figure 2) [21; 22; 23; 24], and solanesol is then formed by the conversion of the SPP pyrophosphate groups to hydroxyl groups.
# Deep Learning and Embedding Based Latent Factor Model for Collaborative Recommender Systems
## 3 Related Work  
### Latent Factor Model for Recommender Systems  
The matrix factorization model is one of the well-known techniques in the latent factor model. It is also a popular research topic in collaborative filtering since it frequently produces a good low-dimensional latent feature representation of users and items. The models generate a latent space from each user and item vector. Finally, it predicts the elements that were missed using the inner product. To demonstrate this concept, consider the latent factors of a user \(p\) and an item \(q\), respectively. Then, the method estimates the approximate rating score, \(\hat{r}_{ij}\), by applying Equation (4) [11].  
\[\hat{r}_{ij}=p_{i}q_{j}^{T}=\sum_{k=1}^{K}(p_{ik}q_{jk}), \tag{4}\]  
where \(K\) represents the embedding dimension that must be taken into account.  
The latent factors \(p_{i}\) and \(q_{j}\) in most MF-based models are calculated using gradient descent methods to minimize the square error between the true value \(r_{ij}\) and the estimated values \(\hat{r}_{ij}\)[11; 14; 23] on Equation (5).  
\[\sum_{i,j}e_{ij}^{2}=\min_{p_{i}A_{ij}}\sum(r_{ij}-\hat{r}_{ij})^{2}. \tag{5}\]  
According to [14], SVD is one of the most effective matrix factorization approaches for enhancing reliability and scalability issues. To improve rating prediction, biased SVD introduced biasing features to the model [14]. By decomposing the rating matrix, the SVD++ algorithm achieved excellent prediction performance [23]. However, these methods are not robust because they require too many iterations to discover the proper latent features. The author of [24] suggested using large values of latent dimension \(K\) to overcome this. However, in the case of a sparse rating matrix, this also has an impact on the recommendation's quality. As a result, we use a dual deep learning network architecture to extract these latent features in our proposed method, which is well suited to tackling those problems.
# Digestate as Sustainable Nutrient Source for Microalgae--Challenges and Prospects
## 6 Downstream Processing
### Dehydration of Cells  
Depending on the product, dehydration of microalgal biomass might be necessary [75]. Following harvesting (and disintegrating) the microalgae culture, the 50- to 200-fold concentrated slurry (5-15% dry weight) must be processed quickly [66]. Dehydration of the microalgae slurry is achieved by solar or spray drying, or lyophilization [66; 75]. Spray drying is a rapid method that yields a powdered product [66]. lyophilization is the mildest of biomass drying methods since it is based on water sublimation from frozen biomass under vacuum and causes little damage to organic materials [75]. Both methods are expensive and are therefore only used for high-value products [66]. For low-value products,solar drying is a cheaper alternative, but it requires large areas, might damage organic compounds, and is weather-dependent [75].