esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Ethyl Fatty Acid Esters
In the wines that underwent MLF, the concentration of diethyl succinate was in the 0.49-2.63 mg/L range (Table 2). Analyzing the influence of different malotactic bacteria inoculation techniques on diethyl succinate concentration, a similar trend to ethyl lactate was noted. The highest concentration of diethyl succinate was always observed in the case of coincoulated wines (1.14-2.63 mg/L) (Table 2), in which the highest rate of MLF was observed [13]. Whereas, in the sequential and spontaneous malolactic process, in which the deacidification rate were significantly lower [13], the concentration of diethyl succinate was also respectively decreased. Similar observations have been described by [PERSON] et al. [9], who noted that sequential inoculations resulted in lower concentrations of ethyl lactate and diethyl succinate than in the case of coinoculation. With regard to other esters, no effects of the malolactic process on the biosynthesis of ethyl hexanoate (fruity, strawberry, and green apple aromas) or ethyl octanoate (fruity, sweet, banana, and pear aromas) were observed. All wines that underwent malolactic fermentation showed a significantly enhanced concentration of ethyl propanoate (pineapple aroma) (Table 2). Different observations have been described by [PERSON] et al. [9], in which a decrease in the concentrations of ethyl hexanoate and ethyl octanoate were reported. Similar to our findings, there was an increase in ethyl propanoate concentration after MLF. Summarizing fluctuations in ethyl fatty acid esters, ethyl lactate and diethyl succinate quantitatively dominated the process, and were the esters showing the greatest increase
## 7 Results on SM Problems
### Sampling the Stable Marriage Lattice
The normalized entropy starts from a value of 0.85 per bit at size 10, decreasing to just above 0.6 per bit as the problem's size grows. Figure 3: Sampling with SML2. (**a**) Average normalized distance \(D_{m}\) varying \(n\); (**b**) entropy. Considering both Figures 3(b) and 3(a), it appears that SML2 samples the stable marriage lattice very well. Considering also the distance, \(D_{m}\) (Figure 3(a)), the possible outcomes appear to be equally distributed along the paths from the top to the bottom of the lattice. To better evaluate the sampling capability of our approach, here, we compare it to a _Markov chain_ approach (MC) [40], defined by using rotations exposed in each stable marriage. More precisely, suppose that \(M_{i}\) is a current marriage. Then, the next marriage, \(M_{i+1}\), is computed as follows:  
* (i) with probability 1/3: it randomly chooses a man and, if he is part of a woman-improving rotation \(\rho\), it moves to \(M_{i+1}=M_{i}/\rho\);
* (ii) with probability 1/3: it randomly chooses a man and, if he is part of a man-improving rotation \(\rho\), it moves to \(M_{i+1}=M_{i}/\rho\);
* (iii) with probability 1/3, it moves to \(M_{  
Since a rotation and its inverse contain the same people, and the probability of picking a particular rotation is proportional to the number of couples it contains, this Markov chain is reversible. This approach converges in exponential time to the uniform distribution over the stable marriages. We consider the entropy and distance from the male-optimal of MC computed on executions, where we vary the number of steps from 10 to 200. While the entropy of MC increases quite rapidly, the distance from the top of the lattice (i.e., from the male-optimal) increases more slowly (see Figure 4 and Figure 4). For each problem instance in the test set, we start MC from the male-optimal marriage and take the stable marriage returned by MC after exactly the same number of steps needed by our algorithm to find a stable marriage for that instance. Then, we measure and compare the entropy and the distance from the male-optimal for MC to those of our algorithm (SML2). While the entropy of MC is roughly the same as that of our algorithm, the distance from the male-optimal achieved by our approach (about 0.5) is, on average, higher that that achieved by MC (about 0.2) (see Figure 4). Summarizing, our approach is efficient, and it has sampling capabilities comparable with a Markov chain approach considering the same number of steps and may even perform slightly better, considering the distance measured from the top or the bottom of the lattice.
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 4 Threats and Vulnerabilities
### Smart Grid Vulnerabilities  
Similarly, vulnerabilities within smart grids can be related primarily to security or safety in addition to other possible sources, such as equipment or communication failures, and natural disasters, with many others being named and described in [2]. Furthermore, smart grid security and safety vulnerabilities are diverse, on a constant rise, and include flaws, weaknesses, and potential risks that, in the event of their occurrence [91], can expose the reliability, performance, and security of the smart grid infrastructure (see Table 12). Such occurrences often cause unauthorized access, data breaches, service interruptions, or compromised grid operations [92]. As a result, aside from cryptography and non-cryptography-based solutions, regular testing, employee training, and adherence to industry best practices can also be an ideal mitigation solution [86]. In the following, these different vulnerabilities are described in detail.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Vulnerability Classification** & **Type** & **Description** & **Enhancements** \\ \hline \multirow{6}{*}{Weak Authentication and Access Controls} & \multicolumn{2}{c}{Weak or outdated mechanisms for access control and authentication, leaving them prone to various attacks} & Access controls, biometric measures, and multi-factor authentication \\ \cline{2-4}  & \multirow{4}{*}{Weak Encryption} & Poor encryption or no encryption at all, leaving communication channels and networks open, exposing data & Advanced symmetric encryption techniques enforced by machine learning algorithms \\ \cline{2-4}  & \multirow{4}{*}{Software Vulnerabilities} & Attackers can exploit security gaps and gain unauthorized access, exploit misconfiguration, execute malicious code, and inject malicious data & \multirow{4}{*}{Constant updates and ethical hacking (pen testing)} \\ \cline{2-4}  & \multirow{4}{*}{Supply Chain Vulnerabilities} & Can disrupt the flow of data and services, which can affect the power outage and result in a blackout & Regulatory compliance, secure communication, cyber–physical security measures, and risk assessment \\ \cline{2-4}  & \multirow{4}{*}{Equipment and System Vulnerabilities} & Flaws in the technological infrastructure, software, or hardware/device components to compromise the smart grid} & Response strategies, regulatory compliance, and security awareness training \\ \cline{2-4}  & & Flaws in the technological infrastructure, software, or hardware/device components to compromise the smart grid & Response strategies, regulatory compliance, and security awareness training \\ \hline \end{tabular}
\end{table}
Table 12: Security and safety-based vulnerabilities within smart grids.
## 2 Electricity-Gas-Heat Integrated Energy System Model
### Constraints  
#### 3.4.1 Power Flow Constraints  
The power network model mainly includes three parts [10]: constraints on node power balance, DC power flow constraints and minimum start-stop time constraints.  
(1) Constraints on node power balance  
\[\begin{array}{l}\sum\limits_{i\in\Omega_{\rm F}}P_{\rm F,i,t}+\sum\limits_{i \in\Omega_{\rm GT}}P_{\rm GT,i,t}+\sum\limits_{i\in\Omega_{\rm Wind}}P_{\rm Wind,i,t}+\sum\limits_{i\in\Omega_{\rm CHP}}P_{\rm CHP,i,t}+\sum\limits_{l\in i}P_{ l,t}\\ =P_{\rm load,i,t}+\sum\limits_{i\in\Omega_{\rm P2G}}P_{\rm P2G,i,t}+\sum\limits_{i \in\Omega_{\rm EB}}P_{\rm EB,i,t}+P_{\rm CCS,t}\end{array} \tag{19}\]  
where \(\Omega_{\rm F}\) is the set of all coal-fired generators, \(\Omega_{\rm GT}\) represents the collection of all gas turbines, \(\Omega_{\rm Wind}\) is the collection of all wind farms, \(\Omega_{\rm CHP}\) is the set of all CHP units, \(\Omega_{\rm EB}\) is the set of all electric boilers, \(\Omega_{\rm P2G}\) is a collection of all P2G devices, \(l\)\(\in\)\(i\) is all the lines connected to node \(i\), \(P_{\rm load,i,t}\) is the load power of node \(i\) at time t.  
(2) Constraints on upper and lower limits of unit power  
\[u_{i,t}P_{i}^{\rm min}\leq P_{i}\leq u_{i,t}P_{i}^{\rm max} \tag{20}\]  
where \(P_{i}^{\rm max}\) and \(P_{i}^{\rm min}\) are, respectively, the maximum and minimum output values of unit \(i\).  
(3) Minimum start-stop time constraints  
\[\left\{\begin{array}{l}\sum\limits_{k=t}^{t+\Omega_{\rm GT}-1}(1-u_{i,k}) \geq T_{\rm off}(u_{i,t-1}-u_{i,t})\\ \sum\limits_{k=t}^{t+\Omega_{\rm on}-1}u_{i,k}\geq T_{\rm on}(u_{i,t}-u_{i,t-1} )\end{array}\right. \tag{21}\]  
where \(T_{\rm off}\) is the minimum shutdown time of the unit, \(T_{\rm on}\) is the minimum shutdown time of the unit.  
(4) Constraint on unit climbing rate  
Unit climb rate constraints include coal-fired units, gas turbines and P2G power stations:  
\[P_{i,t}-P_{i,t-1}\leq u_{i,t-1}(P_{i}^{\rm up}-S_{i}^{\rm up})+S_{i}^{\rm up} \tag{22}\]  
\[P_{i,t}-P_{i,t-1}\leq u_{i,t}(P_{i}^{\rm down}-S_{i}^{\rm down})+S_{i}^{\rm down} \tag{23}\]  
where \(P_{i}^{\rm up}\) and \(P_{i}^{\rm down}\) are, respectively, the lifting and falling output rates of unit \(i\) in continuous operation, \(S_{i}^{\rm up}\) is the lifting output rate of unit \(i\) starting, \(S_{i}^{\rm down}\) is the output reduction rate of unit \(i\) shutdown.  
(5) DC power flow constraints  
\[\left\{\begin{array}{l}\theta_{n}^{\rm min}\leq\theta_{n}\leq\theta_{n}^{\rm max }\\ P_{nl}^{\rm min}\leq P_{nl}\leq P_{nl}^{\rm max}\\ B_{l}(\theta_{n}-\theta_{m})-P_{nl}\ =\ 0\end{array}\right. \tag{24}\]  
where \(\theta_{n}^{\rm max}\) and \(\theta_{n}^{\rm min}\) are the upper and lower limits of power angle of node \(n\), respectively, \(P_{nl}^{\rm max}\) and \(P_{nl}^{\rm min}\) are the upper and lower limits of power on the road connected by node \(n\), \(B_{l}\) is the equivalent susceptance of line 1.
## 3 Validation and Grid Dependency Study
### Validation  
The cylinder boundaries are treated by the local DFD method and the numerical methodology is first validated by flow past a wall-free vibrating circular cylinder at \(Re=150\), \(\zeta=0\), \(m^{*}=\frac{8}{\pi}\) or \(m^{*}=2\). The reduced velocities \(U_{r}\) are varied from 2 to 9. The variation of \(U_{r}\) is achieved by altering the damped natural frequency of the structure \(f_{n}\) while the velocity of the freestream is maintained constant. As shown in Figure 6, the response amplitudes \(A_{ave}\) and their frequencies \(f_{d}/f_{n}\) varied with \(U_{r}\) are presented together with some other referenced numerical results of [PERSON] et al. [34] or [PERSON] et al. [35]. The results of [PERSON] et al. are from two-dimensional simulations while those of [PERSON] et al. are from three-dimensional ones.  
It can be seen from Figure 6 that the present results agree well with those of the referenced studies. At \(m^{*}=\frac{8}{\pi}\), the lock-in regime is between \(U_{r}=4\) and 7; it is broadened to \(U_{r}=8\) at \(m^{*}=2\). The response frequency is almost the same as the damped natural frequency \(f_{n}\) inside the lock-in regime.  
In addition, the current result of the maximum \(A_{ave}\) is compared with other numerical and experimental results in Table 2. The computational results of [PERSON] and [PERSON] [36] are from two-dimensional simulations and the results of [PERSON] [37] are from experiments. The present simulation shows good agreement in terms of the maximum \(A_{ave}\) with other numerical results. However, the maximum \(A_{ave}\) is below the experimental result, whose maximum \(A_{ave}\) is \(A_{ave}\sim 1D\). This discrepancy may be due to the different Reynolds numbers and the absence of turbulence from high-Reynolds-number flows.
# Multi-Step Traffic Speed Prediction Based on Ensemble Learning on an Urban Road Network
## 3 Prediction Methodology
### Detrending  
Since traffic speed time series used in this paper are different spatio-temporal characteristics (i.e., workdays or weekends, peak or off-peak hours), it is reasonable to split speed time series into its mean trends and residuals via detrending algorithm, and develop the model to predict the residual time series. Following the previous literature [36], a simple average method was used to find out the trend, which takes the average of daily traffic  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Representative Feature** & **Descriptions** \\ \hline \(v_{(d,t)}\) & Speed at time \(t\), day \(d\) \\ \(v_{(d-1,t)}\) & Speed at time \(t\), day \(d-1\) \\ \(v_{(d-2,t)}\) & Speed at time \(t\), day \(d-2\) \\ \(v_{(d-3,t)}\) & Speed at time \(t\), day \(d-3\) \\ \(v_{(d-1,t+1)}\) & Speed at time \(t+1\), day \(d-1\) \\ \(v_{(d-2,t+1)}\) & Speed at time \(t+1\), day \(d-2\) \\ \(v_{(d-3,t+1)}\) & Speed at time \(t+1\), day \(d-3\) \\ \(v_{(u,t)}\) & Upstream speed at time \(t\), day \(d\) \\ \(v_{(d,t)}\) & Downstream speed at time \(t\), day \(d\) \\ \(flow_{(d,t)}\) & Flow at time \(t\), day \(d\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Constructed feature candidates for the prediction model.  
Figure 1: The framework of direction strategyspeed series into account in Equation (1). Among, the speed observation at the \(t\)th hour belonging to the \(d\)th day can be formulated as follows:  
\[\left\{\begin{array}{rl}v_{(d,t)}=\frac{1}{24}\sum\limits_{t=1}^{24}v_{(d,t)}+r _{(d,t)}^{2}\\ flow_{(d,t)}=\frac{1}{24}\sum\limits_{t=1}^{24}flow_{(d,t)}+r_{(d,t)}^{flow}\\ \end{array}\right. \tag{1}\]  
where the first item on the right of the equation represents the average speed(flow) of the \(d\)th day; \(r_{(d,t)}^{p}\) (\(r_{(d,t)}^{flow}\)) represents the speed(flow) residual at the \(t\)th hour on the \(d\)th day, which constitutes the speed(flow) residual time series. Next, a predicted model will be introduced to forecast the residuals.
## 5 Results and Analysis  
This section analyzes the performance of the DNN-based MIMO detection model, focusing on the impact of activation functions and training algorithms on SER. The results are then compared to those achieved using conventional MIMO methods (MLD and MMSE).
## 4 Discussion
### Mechanism of the Uniaxial Static Stress Affecting Crack  
The tensile fracture is the main failure mode of rock under a dynamic load. According to the maximum tensile strain failure criterion, rock will fracture if the strain that is caused by explosive dynamic load reaches the dynamic ultimate tensile strain of rock, as shown in Equation (1) [47,48].  
\[\varepsilon_{ds}\geq\varepsilon_{td} \tag{1}\]  
where: \(\varepsilon_{ds}\) is the tensile strain caused by the coupling of dynamic load and static loads and \(\varepsilon_{td}\) is the dynamic ultimate tensile strain of rock.  
The static strain field caused by static stress of \(y\)-axis is:  
\[\begin{split}\varepsilon_{1}=\varepsilon_{sx}=\frac{\mu v_{y}}{ E}\\ \varepsilon_{2}=\varepsilon_{sy}=-\frac{\sigma_{y}}{E}\end{split} \tag{2}\]  
where: \(\varepsilon_{1}\) and \(\varepsilon_{2}\) are the maximum and minimum principal strains on the free surface of the specimen, respectively; \(\varepsilon_{sx}\) and \(\varepsilon_{sy}\) are the static strains in the \(x\)-axis and \(y\)-axis direction that are induced by static stress of \(y\)-axis, respectively.  
According to Equation (2), under the static stress of the \(y\)-axis, the static strain field in the \(x\)-axis and \(y\)-axis direction are tensile strain and compressive strain, respectively.  
Figure 11 shows the maximum and minimum principal strain field on the free surface that are caused by \(y\)-axis static stress [24], which were obtained by the model crater blasting experimental method that was used in this study and a DIC. Clearly, the maximum principal strain field is positive, which is the tensile strain field, whose direction is perpendicular to the static stress while the minimum principal strain field is negative, which is the compressive strain field, whose direction is parallel to the static stress. The experimental results presented in Figure 11, are consistent with the theoretical analysis results in Equation (2). Meanwhile, the direction of the maximum principal strain field is always perpendicular to the static stress during crater blasting (see Figure 10a).  
Due to the effects of the strain field that is induced by uniaxial static stress, the cracks inevitably propagate along the \(y\)-axis direction which eventually leads to an elliptical crack network. That is why the crack network is an ellipse with its long axis that is parallel to the static stress under uniaxial static stress.  
Figure 11: The static strain field that is induced by static stress of \(y\)-axis. (**a**) The minimum principal strain field; (**b**) the maximum principal strain field.
# A Survey on Network Optimization Techniques for Blockchain Systems
## 5 Network Optimization Techniques Used in Blockchain Networks
### Multi-Layer  
Some blockchain frameworks, such as Hyperledger Fabric, allow administrators to manually configure some blockchain parameters, such as block size and block generation interval. Selecting optimum values for these parameters is an area that researchers have exploited to improve blockchain performance. [PERSON] et al. [100] studied the impact of these tunable parameters on the blockchain network and presented a guideline for building an optimum blockchain network. Even though this guideline promises to build a blockchain with an improved throughput, it may not scale well because the guidelines were provided from a network with less than ten peers. Moreover, a configuration that is optimum for a network with tens of peers may not be optimum for a network with hundreds of peers. Table 9 summarizes the relevant works in this category.  
[PERSON] et al. [101] used a Deep Reinforcement Learning (DRL) technique to proactively select a consensus algorithm, block size, interval and producers. They dynamically adjusted the consensus algorithm used by the blockchain network, block-producing peers, the rate at which their system generated blocks and the size of a block. The state-space of their DRL approach includes the size of a transaction, computational strength of a node and data transmission rate. The action taken by the agent is primarily changing either one or multiple parameters (consensus algorithm, block size, producers and interval). As a result, the agent interacts with the environment to increase transactional throughput, finality and more. The usage of consensus algorithms with a smaller traffic footprint led to a net reduction in bandwidth. Even though this work is effective throughput and bandwidth, further investigation should be conducted into multi-agent approaches since a single agent will not scale very well with network growth. Moreover, constantly interacting with the network can hamper its performance. It is, therefore, necessary to investigate how to achieve a trade-off between maximized performance and minimized interaction.
# Cooperative Admission Control with Network Coding in 5G Underlying D2D-Satellite Communication
## Appendix A Theorem 1  
**Proof.** In MANC-batch, the packets' linear combination is transmitted by the satellite/source and D2D in the second stage. Therefore, the size of the \(A\) increases by two except those that already have the maximum size of the acquired \(A\). The size of the transmitter is increased by the one. In the first transmission slot, the size, \(x=\text{arg }max_{ncR}|A_{n}|\)is selected as the transmitter, which results in an increase in the length of the \(A_{n}\) by two and size of \(A_{x}\) increased by one. Now, for the next time slot for the transmission, the \(x\) remains as the transmitter device until there is some other device in the network which has more innovative packets then-current \(x\) device. Now it takes \(|A_{x}|-|A_{n}|\) time slot of transmission for \(n\) device to have the \(A\) same size as of acquired by \(x\) device and selected as the transmitter. So considering rvr =arg \(min_{nc(R/x)}|A_{n}|=\text{arg }max_{nc(R/x)}|D_{n}|\), it takes about \(|A_{t}|-|A_{r\text{rvr}}|\) for transmitter to get select. Two cases will be evaluated further, as shown.  
* \((X-|A_{x}|)\leq|A_{x}|-|A_{r\text{rvr}}|\): After the transmission of the packet from the max transmitter, the size of the \(|A_{x}|\) will be the same as the size to \(X\) and the \(|A_{r\text{rvr}}|\) less in size as compared to the \(|A_{x}|\). So for the rest of the communication, \(x\) in our case will remain the transmitter and will be upper bounded by the timeslots for transmission for device \(r\text{vrr}\) such that \(\frac{1}{2}\text{max}_{ncR}|D_{n}|\).
* \((X-|A_{x}|)\geq|A_{x}|-|A_{r\text{rvr}}|\): The size of the \(|A_{x}|\) becomes equal to the size of the \(|A_{r\text{vrr}}|\) when innovative packet increases by \(|A_{x}|\cdot|A_{r\text{vrr}}|\) time slots of the transmission. So devices \(r\text{vrr}\) and \(x\) will be selected for transmission alternatively. The scheme is that at the one-time slot of transmission, \(x\) be selected, and in other times of transmission, \(r\text{vrr}\) is selected. So the size of \(|A_{r\text{vrr}}|\) increases by three after every two consecutive transmission slots. So packet time upper bounded to satisfy the \(r\text{vrr}\) is defined as \(\frac{1}{2}(|D_{r\text{vrr}}|+|D_{x}|)\).  
Finally, the timeslots cannot be less than \(|X_{c}|\).  
So the upper bound is defined by the Equation (17).
## 2 Description of Specimens and Test  
### Description of Process to Make Specimens  
The manufactured architectural finishing material specimens were produced using a small press designed for the preparation of cone calorimeter specimens. The specimen was made by stirring flame retardants in the cellulose slurry used in the Large Wet Cellulose 3D Printer study [11]. To ensure uniform pressure across the specimen, the sample was placed in the mold, and a constant pressure was applied through compression. The mold size was designed to be 100 mm \(\times\) 100 mm, following the specifications of the cone calorimeter, with a height of 50 mm to prevent the sample from escaping outside the mold due to pressure. Figure 2 depicts the configuration of the small press used to create architectural finishing material specimens.  
The specific flame retardants of expandable graphite and magnesium hydroxide were added to improve the fire performance of cellulose in this study for the following reasons:  
* Expandable graphite has been utilized as a flame retardant based on its physical mechanism in various architectural materials research studies [15; 16; 17; 18]. Expandable graphite expands into a carbon layer upon heating, thus protecting the bottom of the specimen and simultaneously inhibiting the external spread of fire [18; 19; 20]. An 80-mesh expandable graphite was chosen in this study due to its significant reduction in total heat release compared to expandable graphite of other particle sizes [21];
* Magnesium hydroxide has been utilized as a flame retardant with a chemical mechanism in various studies [22; 23; 24; 25; 26]. Magnesium hydroxide was added to enhance the fire performance through an endothermic reaction during the heat reaction. The endothermic reaction of magnesium hydroxide is expressed in Equation (1) [27; 28; 29]. \[Mg(OH)_{2}\to MgO+H_{2}O\;1244\,\sim 1450J/g\] (1)  
In this study, a cellulose slurry of a constant mass was prepared by adding the same expandable graphite (30 g) to evaluate the fire performance and analysis of toxicity gas emissions based on variations in the content of added magnesium hydroxide. The mixture of cellulose architectural finishing material was created by increasing the magnesium hydroxide content in 10 g increments, thus resulting in mixtures with 0 g, 10 g, 20 g, and 30 g. Table 1 presents the compositions of the specimens.  
Figure 2: Schematics of (**a**) small press and (**b**) mold.