esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Genetic Algorithm
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Model Parameters**} & \multicolumn{6}{c}{**Values**} \\ \hline Luenberger Q Matrix—Manually Tuned & \multicolumn{6}{c}{\(B*B^{T}\)} \\ \hline Luenberger R Matrix—Manually Tuned & \multicolumn{6}{c}{\(\begin{bmatrix}0.5&0&0\\ 0&0.5&0\\ 0&0&0.5\end{bmatrix}\)} \\ \hline Luenberger R Matrix—Manually Tuned & \multicolumn{6}{c}{\(\begin{bmatrix}-14.308&-1.816&0.944\\ -1.055&0.379&0.069\\ 25.547&2.971&-1.546\\ 7.138&0.045&-0.206\\ -0.330&13.422&7.888\\ -2.164&60.376&132.850\\ 0.0453&10.981&0.439\\ -0.206&0.439&37.805\end{bmatrix}\)} \\ \hline Parameters for PSO algorithm & \multicolumn{6}{c}{Values} \\ \hline N\_particles & \multicolumn{6}{c}{22} \\ Swarm Size & \multicolumn{6}{c}{20} \\ c1 & \multicolumn{6}{c}{1} \\ c2 & \multicolumn{6}{c}{1} \\ MaxIter & \multicolumn{6}{c}{2000} \\ Wmin & \multicolumn{6}{c}{[0, 0, 0, 0, 0, 0, 0, 0, 0.001, 0.001, 0.001, 0.001, 0, 0, 0, 0, 0, 0, 0, 0, 0.001, 0.001]} \\ Wmax & \multicolumn{6}{c}{[1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 0.01, 0.01,1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 0.01, 0.01]} \\ \hline State Feedback Gain (K)—PSO & \(\begin{bmatrix}-370&-13.1&191.9&1097.4&-50.3&-32.1&-212&-28.6\\ 70.1&-3.7&-23.5&-214.8&-299.8&-195.4&-1051.5&-152.2\\ 12.6&0&-0.2&-76&181&26.8&94.5&-243.8\end{bmatrix}\) \\ \hline Luenberger Observer Gain (Lo)—PSO & \multicolumn{6}{c}{\(\begin{bmatrix}55.987&-21.564&1.663\\ 2.671&0.320&1.789\\ 53.243&-14.659&-6.605\\ 255.968&-0.058&0.005\\ -27.694&-125.848&190.491\\ -11.456&30.085&-31.376\\ -0.058&198.186&-0.120\\ 0.005&-0.120&310.408\end{bmatrix}\)} \\ \hline
**Parameters for GA-Hybrid** & \multicolumn{6}{c}{**Values**} \\ \hline PopNum & \multicolumn{6}{c}{100} \\ ChromNum & \multicolumn{6}{c}{9} \\ CrossVal & \multicolumn{6}{c}{80} \\ MutVal & \multicolumn{6}{c}{60} \\ State Feedback Gain (K)–GA-Hybrid & \(\begin{bmatrix}-0.458&-0.064&1.049&3.095&-0.092&-0.142&-0.658&-0.016\\ 0.108&-0.014&-0.181&-0.882&-1.364&-1.575&-6.282&-0.468\\ 0.032&-0.003&-0.020&-0.331&0.707&0.003&0.214&-0.297\end{bmatrix}\) \\ \hline Parameters for GA & \multicolumn{6}{c}{Values} \\ \hline PopNum & \multicolumn{6}{c}{50} \\ ChromNum & \multicolumn{6}{c}{18} \\ CrossVal & \multicolumn{6}{c}{80} \\ MutVal & \multicolumn{6}{c}{60} \\ MaxGen & \multicolumn{6}{c}{5000} \\ \hline State Feedback Gain (K)—PSO & \(\begin{bmatrix}-0.718&-0.112&0.776&3.320&-0.131&-0.080&-0.637&-0.041\\ 0.155&-0.029&-0.116&-0.731&-0.671&-0.485&-2.766&-0.282\\ 0.027&-0.006&-0.010&-0.241&0.309&0.070&0.321&-0.538\end{bmatrix}\) \\ \hline \hline \end{tabular} 

## Appendix D Simulation Parameters for Linear Quadratic Gaussian
## 1 Introduction
The hybrid prediction model comprehensively considers the advantages of each model, realizes the complementarity between models in the construction process, and improves prediction accuracy and decision rationality ([PERSON] et al., 2019; [PERSON] et al., 2020). The health condition of ship mechanical systems will directly affect safe and reliable navigation by an autonomous ship. Therefore, using the system running-state parameters to predict and track system health conditions is an important means for achieving system health management. To produce these assumptions, a system health condition prediction model based on SHI was proposed in this study. This model is an integrated method that can take into account the interactions between components, the impact of uncertainty, and the effects on function output on a system's degradation. The main contributions of this study are summarized as follows:  
(1) The system-level adaptive SHI construction method is proposed. The fixed HI construction was transformed into automatic SHI extraction with the DKPCA and SAE fusion method, which improved the expression ability of the HI for system degradation characteristics. (2) A method of decomposing SHI according to specified degradation characteristics is proposed. VMD was used to separate the SHIs into global degradation, local self-healing, and local interference. (3) A dynamic hybrid prediction model was proposed. Considering the different characteristics of the decomposed signal, a relevance vector machine (RVM) and long short-term memory (LSTM) were used to achieve hybrid prediction. The remainder of this article is organized as follows. Section 2 describes the typical problem of ship mechanical systems and the framework. The proposed health state prediction model is described in Section 3. The parameters of a fuel-oil supply system were chosen as examples to verify the feasibility of the method, as described in Section 4. Conclusions are drawn in Section 5, along with discussions of future challenges as well as opportunities for machinery prognostics.
# Energy Reduction Multipath Routing Protocol for MANET Using Recoil Technique
## 4 Mathematical Support of Proposed Model for Results
### Lemma-3  
The energy consumption in routing protocol increases with node mobility in network in both proactive and reactive protocols. It can be represented using Equation (26).  
\[M_{1}>M_{2},\textit{then}\ E(M_{1})>E(M_{2}) \tag{26}\]  
**Proof.** The energy consumed by the protocol, the summation of energy consumption in performing route discovery (RD) and energy consumed in routing maintenance (RM), that is, the cost paid in the RD and RM processes, can be illustrated using Equation (18).  
\[E^{p}=\ E_{RD}+\ E_{RM} \tag{27}\]  
where \(E^{p}\), represents energy consumed in routing protocol, \(E_{RD}\) notation used for energy consumed in route discovery process, and \(E_{RM}\) stands for route maintenance. The energy in route discovery would be the cost for the RREQ packet and RREP packet during RD process. It can be expressed as below in Equation (28)  
\[\left\{\begin{array}{cc}\sum_{R_{i}=1}^{R_{max}}(E_{R_{i}})R_{i}&\textit{if }\textit{ no }\textit{RREP received}\\ E_{\textit{rrep}}+\sum_{j=1}^{j=n_{\textit{rrep}}}(RREP)_{j}&\textit{if }\textit{R }_{\textit{rrep}}=1\\ \sum_{R_{i}=1}^{R_{\textit{rrep}}}(E_{R_{i}})R_{i}&+\sum\limits_{j=1}^{j=n_{ \textit{rrep}}}(RREP)_{j}&\textit{otherwise}\end{array}\right. \tag{28}\]  
where, \(R_{\textit{rrep}}\) and \(\textit{rrep}\) are same and takes the value \(\textit{rrep}=1,\ 23,\ \ldots max\). The energy consumption in RM process completion can be expressed as below in Equation (29).  
\[E_{RM}=E_{l-mon}+\ E_{LLR}+\ \sum\limits_{k=0}^{k=j}(RREP)_{k} \tag{29}\]  
where \(E_{l-mon}\) represents the link monitoring energy consumed in protocol. The total energy consumed can be calculated as the sum of Equations (28) and (29), which is expressed by Equation (27). When the mobility of nodes increases, the routing overhead increases along with corresponding energy consumption in the process. Hence the value of Equation (27) holds the inequality expressed in (26).  
The first order derivative \(E^{\prime}(N)\geq 0\), shows that the packet delay function increases with number of nodes. From the result (Figure 13c), the slope of the tangent line would be the same as the slope of the equation of the best fit line curve \(y=3.12x+51.6\), the equation of tangent of the curve. This is 3.12, a positive value. Here, the energy function is also increasing with mobility of nodes, hence the result is in agreement \(\square\).
## 3 Results and Discussion
### Optimization of the Extraction Conditions Using BBD
#### 3.2.2 Response Surface Analysis  
Three-dimensional response surfaces and two-dimensional contour plots were used to visualize the regression equations [19]. The 3D response surface explains the relationship between the dependent variables and the interaction between two test variables when the other factors are kept constant at a zero level [20]. The shape of the contour plots reflects the mutual interaction effects between the variables. A circular plot indicates an insignificant interaction, whereas an elliptical contour plot indicates significant influences by the test parameters [21].  
The effects of ultrasonication temperature (A) and power (B) on the yield were determined when the time (C) was set at a zero level. The yield increased with the increase in temperature from 50 to 66 \({}^{\circ}\)C and the yield decreased when the temperature exceeded 66 \({}^{\circ}\)C (Figure 2A). The 2D contour plot (Figure 2B) indicated that the mutual interaction between A and B was not significantly influential. The effects of the ultrasonication temperature (A) and time (C) with ultrasonication power (B) fixed at a zero level showed significant yields in the 2 to 2.6 h range (Figure 2C). The mutual interaction between the ultrasonication temperature (A) and ultrasonication time (C) is shown in Figure 2D, which indicated their significant influence. The yields increased as the ultrasonication power (B) increased from 160 to 205 W and then decreased as the power increased further from 205 W to 240 W. The highest polysaccharide yield was achieved with a power setting of 205 W and ultrasonication duration of 2.6 h (Figure 2E). The 2D contour plot indicated a significant mutual interaction between power (B) and time (C) (Figure 2F).
## 3 Results
### Beef Production  
According to Eurostat, in 2018, the EU production of beef reached 7.9 million tonnes. The main beef producing countries were France and Germany [2]. Significant heterogeneity between production systems across the EU was observed [47] to a greater extent than in other livestock systems. [PERSON] (2021) documented 25 different production systems [47], ranging from differences in feed (pasture to grass-fed), housing systems, economic production systems, and breeds.  
According to [PERSON] et al. (2010), it has been estimated that around half of all EU beef production comes from culled dairy cows and that the other half comes from systems specialized in beef production [12]. Multiple studies highlight that, compared to the main livestock systems, the production of beef carries the highest environmental load per kilogram of meat produced [12; 48]. Data presented below are from [PERSON] et al. (2010), who conducted an LCA on four types of beef production systems in the EU [12] and is further complemented by a beef production study by [PERSON] et al. (2014) [20]. [PERSON] et al. (2010) showed that on average in the EU, 59.2 MJ/kg of beef was required for a suckler cow-calf and 43.73 MJ/kg for a dairy bull (Figure 3 and Table 10).  
Considering that around 60% of beef production in the EU comes from dairy bulls and 40% from suckler cows-calves, a rough estimate would suggest that 30-40% of all energy used in livestock production is associated with feed and fertilizers. European cows are generally grown on a combination of home-grown grass and cereals as well as imported soy meal and minerals [12]. However, it is important to note that the energy input in feed can vary significantly depending on whether a cow is fed on grass-pasture or concentrate-confinement, as pasture requires much less energy input for its cultivation and fertilization operations [36].  
On the other hand, this rough estimate would suggest that 60-70% of energy consumption in beef production systems is associated with on-farm activities. [PERSON] et al. (2010) provided a breakdown of direct energy use. His study highlighted that on-farm  
Figure 3: Energy inputs for beef production slaughter weight MJ/kg (based on [12; 20]).  
diesel use accounts for 71% of direct energy use, which can mainly be attributed to manure management and field operations, while on-farm electricity used in stables and housing accounts for 17% and crop processing for 11% of total energy requirements (Figure 4).  
It is important to note that different production systems have different requirements and that there is a scarcity of data across a variety of methodologies for calculating energy use in beef production systems. Especially in beef production systems, significant trade-offs appear to exist between feed, land use, and energy requirements [49]. For instance, some studies suggested that grass-fed beef is more environmentally friendly than non-grass-fed beef but is likely to have a higher land-use footprint [49]. According to the Agricultural and Food Research Council, methods for predicting energy use in beef production systems are outdated [50].
# Mathematical Apparatus of Optimal Decision-Making Based on Vector Optimization
## 2 Statement of a Problem: Creation of the Mathematical Model \"Acceptance of an Optimal Solution\"
### Vector Optimization with a Criterion Priority
#### 3.2.1 Axioms and the Principle of Optimality of Vector Optimization with a Criterion Priority
In the vector problem of Equations (1)-(4), in which \"max\" and \"min\" are part of the criteria, the mathematical operation \"opt\" consists of the definition of a point \(X^{0}\) and the maximum \(\lambda^{0}\) bottom level to which all criteria measured in relative units are lifted:  
\[\lambda^{0}\leq\lambda_{k}\Big{(}X^{0}\Big{)}\ =\ \frac{f_{k}(X)-f_{k}^{0}}{f_{k}^{ *}-f_{k}^{0}},k\ =\ \overline{1,K}, \tag{24}\]  
i.e., all criteria of \(\lambda_{k}(X^{0})\), \(k=\overline{1,K}\) are equal to or greater than the maximum level of \(\lambda^{0}\) (therefore \(\lambda^{0}\) is also called the guaranteed result). Theorem 2: (The theorem of the most inconsistent criteria in a vector problem with the set priority). If in the convex vector problem of mathematical programming of Equations (1)-(4) the priority of the \(q\)th criterion of \(p_{k}^{q}\), \(k=\overline{1,K}\), \(\forall q\in K\) over other criteria is set, at a point of an optimum \(X^{0}\in S\) obtained on the basis of normalization of criteria and the principle of guaranteed result, there will always be two criteria with the indexes \(r\in K\), \(t\in K\), for which the following strict equality holds:  
\[\lambda^{0}\ =\ p_{k}^{r}\lambda_{r}\Big{(}X^{0}\Big{)}\ =\ p_{k}^{t}\lambda_{t} \Big{(}X^{0}\Big{)},r,t,\in K, \tag{25}\]  
and other criteria are defined by inequalities:  
\[\lambda^{0}\leq p_{k}^{q}\Big{(}X^{0}\Big{)},k\ =\ \overline{1,K},\forall q\in K,q \
eq r\
eq t. \tag{26}\]  
Criteria with the indexes \(r\)\(\in\)K, \(t\)\(\in\)K for which the equality of Equation (38) holds are called the most inconsistent. Proof. Similar to Theorem 2 [25]. We note that in Equations (25) and (26), the indexes of criteria \(r\), \(t\in K\) can coincide with the \(q\in K\) index. Consequence of Theorem 1, about equality of an optimum level and relative estimates in a vector problem with two criteria with a priority of one of them. In a convex vector problem of mathematical programming with two equivalent criteria, solved on the basis of normalization of criteria and the principle of the guaranteed result, at an optimum point \(X^{0}\) equality is always carried out at a priority of the first criterion over the second:  
\[\lambda^{0}\ =\ \lambda_{1}\Big{(}X^{0}\Big{)}\ =\ p_{2}^{1}\Big{(}X^{0} \Big{)}\lambda_{2}\Big{(}X^{0}\Big{)},X^{0}\in S,\ \textit{where}\ p_{2}^{1}\Big{(}X^{0}\Big{)}\ =\ \lambda_{1}\Big{(}X^{0}\Big{)}/\lambda_{2}\Big{(}X^{0}\Big{)}, \tag{27}\]  
and at a priority of the second criterion over the first:  
\(\lambda^{0}=p_{1}^{2}(X^{0})\lambda_{1}(X^{0})=\lambda_{2}(X^{0})\), \(X^{0}\in S\), where \(p_{1}^{2}(X^{0})=\lambda_{2}(X^{0})/\lambda_{1}(X^{0})\).
## 3 Observational Data Description, Assimilation and Experimental Design
### GNSS Observations for the Livorno and Silvi Marina Test Cases  
ZTD time series at 30 s resolution were estimated for all the geodetic permanent stations within the WRF domain to perform data assimilation experiments. For the selected events the data of 375 permanent stations were available (Figure 4). The iono-free phase range combination of dual-frequency GPS-only observations for three days including the selected events were processed in PPP mode. An accuracy assessment [37] of the GNSS-derived ZTD time series was performed by comparison with 43 radiosonde launches from eight Italian radiosounding datasets. This comparison yielded a mean difference in ZTD of 0.3 cm and a standard deviation of 1.46 cm (not shown).  
GNSS ZTDs are used also to validate the retrieved maps of InSAR ZTD in the Silvi Marina test case. The statistics of the differences for this case are reported in Table 1. A good agreement is reached between GNSS and InSAR after orbital error removal, with a standard deviation of the residuals in the order of few cm.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**before Orbital Error Removal**} & \multicolumn{1}{c}{**after Orbital Error Removal**} \\ \hline no. of data & 53 & 53 \\ mean [cm] & 1.24 & \(-\)0.14 \\ standard deviation [cm] & 4.87 & 2.42 \\ min [cm] & \(-\)9.54 & \(-\)5.19 \\ max [cm] & 9.89 & 4.46 \\ \hline \hline \end{tabular}
\end{table}
Table 1: InSAR Zenith Total Delay (ZTD) vs. GNSS ZTD–Silvi Marina test case.
###### Abstract  
This paper aims to extend the limited bandwidth of phosphorescent white LEDs for a multiple-input single-output (MISO) visible light communication (VLC) system. A proposed LED arrangement model is presented, resulting in improved results as compared to those previously discussed in the literature. In this paper, the impact of the receiver field of view (FOV) angle, the LED transmission angle, and the number of LED arrays used on the transmission are studied at different speeds. The system performance is measured by the signal to noise ratio (SNR) and the corresponding bit error rate (BER) at different data rates. The obtained results show that the proposed model is able to improve the illumination uniformity across the room with a higher SNR using the same number of LEDs in the investigated indoor environment. The paper also introduces a pre-equalization circuit in the transmitter end in order to extend the limited bandwidth of the used white LEDs. This bandwidth extension using the proposed circuit results in an increased data rate with the aid of a blue filter. A number of experiments are executed to optimize the key parameters for maximum bandwidth enhancement. The proposed circuit offers 28% bandwidth enhancement over the most recent study in this area while eradicating the BER at 200 Mb/s when compared to un-equalized LED circuits.  
bandwidth; equalization; illuminance; visible light communications (VLC); white light emitting diode (LED) +
Footnote †: journal: Applied Physics Letters
# Corrosion Monitoring Techniques in Subcritical and Supercritical Water Environments
## 3 Invasive Corrosion Monitoring Techniques
### Electrochemical Impedance Spectroscopy
In the similar simulated solutions of the primary circuit coolant of PWRs, the rate of metal atom conversion to cations at the interface between the alloy substrate and the passivation oxide film for nickel-based alloy 600 is relatively lower, ranging from 1.5-4 \(\times\) 10\({}^{-12}\) mol\(\cdot\)cm\({}^{-2}\cdot\)s\({}^{-1}\), than that of the stainless steel 304 [108]. The relevant content, such as point defect theory and electrochemical impedance spectroscopy analysis, has been detailed in our previous articles [1; 110] and will not be further elaborated here.
# Evaluating Methods for the Identification of Off-Chain Transactions in the Lightning Network
## 2 Background
### Networked Payment Channels  
A single payment channel is used by two entities to exchange funds with each other. The Lightning network is formed by having many such interconnected channels between different nodes. Thanks to such a network, users can pass funds to those that they do not have a direct channel with. For this to work, there are additional mechanisms that allow for payments to be routed across other users' payment channels. As explained in the previous section, the payment channels used in the LN do not require trust between the two endpoints of the channel. There are also mechanisms that make sending payments across channels not requiring the sender to trust the intermediary nodes along the path. The construction that enables this is called Hashed Timelock Contracts (HTLC). It creates contracts where intermediary nodes have a guarantee that they can get funds from the sender if they first transfer it to the receiver. By having the intermediary nodes first pay the receiver and then get the funds from the sender, one stops the intermediary nodes from not passing the funds along to the receiver. If the intermediary was sent the funds first, they could simply not pay the receiver and keep the funds to themselves. HTLC uses a hash function with an input/preimage R; the receiver inputs R into the function and gets a hash H which is given to the sender. The sender then promises to pay the intermediary if they can provide R that generates H. The intermediary then makes the same promise to pay the receiver if they can provide a R that generates H. Now a chain of promises has been created, each promise guarantees to pay an amount to the receiver that can reveal the input R which generates H. Because it was the receiver who created H using R, they know R and can, therefore, give it to the intermediary and get the funds from them. R has now been disclosed to the intermediary, which in turn can use it to get the funds from the sender as previously promised. This can easily be extended to include more intermediaries.  
The promise to pay someone in exchange for disclosing R generating H is not simply a promise. It is a part of a transaction which needs R to be valid. When the sender promises to pay the intermediary, the sender is no longer in control of the funds because a transaction containing this clause has been created and given to the intermediary. Thus, the funds are in a way transferred in a manner and direction one would expect (from sender to intermediary, then intermediary to receiver), but with the clause of providing R generating H to be claimed. A timelock is used to ensure that if no R is provided, the funds can be returned to the creator of the transaction, i.e., the one who made the promise to pay if provided the correct R.