esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Controller Design of AEBS  
### Optimization Index Design of Model Predictive Controller  
The Time to Collision (_TTC_) and Warning Index (_WI_) is the key evaluation index of _ACC_ and _FCW_. To get the multi-performance evaluation of the model predictive controller, a quantization function of _TTC_ and _WI_ needs to be built and unified. _TTC_ is the collision time index, which is designed based on the relative distance and can be expressed as follows:  
\[TTC_{i}=\frac{S_{Long,Diff,i}}{u_{Diff,i}} \tag{2}\]  
Normally, a safe distance requires the _TTC_ to be larger than 3.  
Similarly, _WI_ is the warning index, which warns the test vehicle based on comprehensive values of several different vehicle state variables, and can be expressed as follows:  
\[WI_{i}=\frac{S_{Long,i}-S_{Long,Bk,i}}{S_{\text{Long,Wi},i}-S_{Long,Bk,i}} \tag{3}\]  
\[\begin{split} S_{Long,Bk,i}&=u_{Long,0}T_{Bk, \text{Delay}}+u_{Long,0}T_{Bk,\text{Cmd}}\\ &\quad\quad\quad\quad+\frac{1}{2}J_{0}^{T_{Bk,\text{Cmd}}}\,\dot{u }_{Long}dt\\ &=u_{Long,0}T_{Bk,\text{Delay}}+\frac{u_{Long,i}^{2}-(u_{Long,i }-u_{Long,0})^{2}}{2t_{Bk,\text{max}}}\\ \end{split} \tag{4}\]  
\[\begin{split} S_{Long,\text{Wi},i}&=u_{Long,0}T_{Bk, \text{Delay}}+u_{Long,0}T_{Bk,\text{Cmd}}+\frac{1}{2}\int_{0}^{T_{Bk,\text{Cmd} }}\,\dot{u}_{Long}dt+u_{Long,0}T_{\text{Resp,Delay}}\\ &\quad\quad\quad\quad=\frac{u_{Long,i}^{2}-\left(u_{Long,i}-u_ {Long,0}\right)^{2}}{2t_{Bk,\text{max}}}+u_{Long,0}T_{Bk,\text{Delay}}+u_{ Long,0}T_{\text{Resp,Delay}}\end{split}, \tag{5}\]  
where \(S_{Long,Bk,i}\) is the integrated distance between the vehicle with AEBS and the collision target between the braking start and the end. \(S_{\text{Long,Wi},i}\) is the distance between the vehicle with AEBS and collision target between the brake warning start and the end. \(u_{Long,0}\) is the longitudinal speed at the  
Figure 1: Hierarchical control structure of AEBS (Advanced Emergency Braking System).  
moment when braking starts. \(u_{Long,i}\) is the longitudinal speed at the moment when braking stops. \(u_{Long,i}\) is the equivalent maximum mean brake acceleration. \(T_{Bk,\text{Delay}}\) is the system gap delay, which is given by the brake system hardware gap. \(T_{Bk,\text{Cmd}}\) is the system delay, which is given by the brake system hardware and hydraulic parts. \(T_{\text{Resp,Delay}}\) is the delay in human response between recognition and manipulation.  
For convenience, these two indexes are unified as follows:  
\[TTC_{i}^{-1}=\frac{u_{i}}{S_{Long,i}} \tag{6}\]  
Taking \(TTC_{Third}^{-1}\) as the safe threshold for \(TTC^{-1}\), \(TTC^{-1}\) can be unified as follows:  
\[Idx_{Norm,TTC}=\frac{\left|TTC^{-1}\right|}{\left|TTC_{Thrd}^{-1}\right|}. \tag{7}\]  
Similarly, taking \(WI_{Third}\) as the safe threshold for \(WI\), \(WI\) can be unified as follows:  
\[Idx_{Norm,WI}=\frac{\left|WI_{\text{max}}-WI\right|}{\left|WI_{\text{max}}-WI_ {Thrd}\right|}. \tag{8}\]
# RGCLN: Relational Graph Convolutional Ladder-Shaped Networks for Signed Network Clustering
## 4 Experiments
### Evaluation Metrics  
To evaluate the performance of various algorithms on different datasets, we used two widely used metrics, namely the adjusted Rand index (ARI) [40] and the normalized mutual information (NMI) metric [41].  
Given a set of \(n\) objects \(S=\{O_{1},O_{2},\ldots,O_{n}\}\), let the external evaluation criteria be \(U=\{u_{1},\ldots,u_{R}\}\), and the clustering result be \(v=\{v_{1},\ldots,v_{C}\}\), satisfying \(\bigcup_{i=1}^{R}u_{i}=S=\bigcup_{i=1}^{C}u_{i}\), \(u_{i}\cap u_{i^{\prime}}=\emptyset=v_{i}\cap v_{i^{\prime}}\), where \(1\leq i\
eq i^{*}\leq R\) and \(1\leq j\
eq j^{*}\leq C\). Then set four statistics. \(a\) is in \(U\) belonging to the same category and also belongs to the same category in \(V\); \(b\) is the number of data point pairs that belong to the same category in \(U\) but are different in \(V\). \(c\) is the number of data point pairs that do not belong to the same category in \(U\) but belong to the same category in \(V\), and \(d\) is the number of data point pairs that do not belong to the same category in \(U\) but also do not belong to the same category in \(V\). The Rand coefficient [42] is defined as:  
\[RI=\frac{a+d}{a+b+c+d} \tag{7}\]  
[PERSON] and [PERSON] proposed the adjusted Rand index in 1985. The adjusted Rand index assumes that the hyper distribution of the model is a stochastic model. Then, the adjusted Rand index is defined as:  
\[\begin{split} E(RI)=E\Bigg{(}\sum_{ij}\Bigg{(}\begin{array}{c} n_{ij}\\ 2\end{array}\Bigg{)}\Bigg{)}=\Bigg{[}\sum_{i}\Bigg{(}\begin{array}{c}n_{i}\\ 2\end{array}\Bigg{)}\sum_{j}\Bigg{(}\begin{array}{c}n_{j}\\ 2\end{array}\Bigg{)}\Bigg{]}/\Bigg{(}\begin{array}{c}n\\ 2\end{array}\Bigg{)}\\ \max(RI)=\frac{1}{2}\Bigg{[}\sum_{i}\Bigg{(}\begin{array}{c}n_{i}\\ 2\end{array}\Bigg{)}\sum_{j}\Bigg{(}\begin{array}{c}n_{j}\\ 2\end{array}\Bigg{)}\Bigg{]}\\ \\ ARI=\frac{RI+E(RI)}{MAX(RI)-E(RI)}\end{split} \tag{9}\]  
In order to evaluate the similarity between the real partition and the partition obtained by using the algorithm to divide the network, NMI is widely used for evaluation. Assuming that A and B are two partitions of a network, the NMI between A and B is written as:  
\[NMI=\frac{-2\sum_{i=1}^{C_{A}}\sum_{j=1}^{C_{B}}C_{ij}\log\big{(}C_{ij}N/C_{i }.C_{j}\big{)}}{\sum_{i=1}^{C_{A}}C_{i}\log\big{(}C_{i}/N\big{)}+\sum_{j=1}^{C _{B}}C_{j}\log\big{(}C_{j}/N\big{)}} \tag{10}\]  
where \(N\) is the number of network nodes, and \(C\) is the fusion matrix. \(C_{ij}\) equals the number of nodes shared by community \(i\) in partition \(A\) and by community \(j\) in partition \(B\). \(C_{A}(C_{B})\) is the number of communities in structure \(A(B)\) and \(C_{i}.(C_{j})\) is the sum of elements of \(C\) in row \(i\)(column \(j\)). NMI(A,B) = 1 means that the \(A\) and \(B\) partitions are exactly the same.
# Analysis and Experimental Test of Electrical Characteristics on Bonding Wire
## 3 Frequency Domain Simulation
### Influence of Span on S Parameter of Bonding Wire  
With the development of the narrow spacing (fine-pinch) and the stacked package, the ratio of low radian and long span bonding wire are also rising. The span L is the horizontal distance of interconnection, which is the most important geometric parameter in the bonding wire. While keeping the other parameters unchanged, the bonding wire span L was selected as a variable. The span scanning ranged from 2000 \(\upmu\)m to 4500 \(\upmu\)m. In addition, the interval was 500 \(\upmu\)m: 2000 \(\upmu\)m, 2500 \(\upmu\)m, 3000 \(\upmu\)m, 3500 \(\upmu\)m, 4000 \(\upmu\)m and 4500 \(\upmu\)m. The sweep frequency ranged from 1 MHz to 20 GHz.  
Figure 7: **(a)** S11 of bonding wire with different diameter; **(b)** S21 of bonding wire with different diameter.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Diameter/\(\upmu\)m** & **15** & **20** & **25** & **30** & **35** & **40** \\ \hline Min S21 (Insertion Loss)/dB & \(-\)9.42 & \(-\)8.87 & \(-\)8.71 & \(-\)8.55 & \(-\)8.37 & \(-\)8.16 \\ Corresponding frequency/GHz & 17.53 & 17.43 & 17.38 & 17.38 & 17.33 & 17.18 \\ Passing signal/incoming signal/\% & 33.80 & 36.01 & 36.67 & 37.38 & 38.16 & 39.06 \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of S21 extremum under different diameter.  
The results of S11 and S21 with the span and frequency of the bonding wire are shown in Figure 8a,b ranging from 1 MHz to 20 GHz.  
According to the trends of return loss and graph of insertion loss, the transmission quality of the signal decreases with the rise of the span because of the increase of the span and the length of the bonding wire. Figure 8a is the logarithmic graph of return loss S11. Taking half the power point as an example, when the longitudinal coordinate was \(-\)3 dB, the horizontal coordinate of span 2500 \(\upmu\)m bonding wire stood at 7 GHz, while the coordinate of span 5000 \(\upmu\)m was 3.65 GHz. The working frequency also increased by 91%. In the insertion loss graph, the working band of the bonding wire with a span of 2500 \(\upmu\)m was higher than 3.36 GHz in the \(-\)3 dB (101%). Figure 8b is the insertion loss S21 logarithmic coordinate graph. With the increase of frequency, the insertion loss grows and the signal transmission performance drops. The S21 extreme points of different spans were extracted (as shown in Table 4). When the span was 2500 \(\upmu\)m at 25.12 GHz, the S21 had the minimum value of \(-\)8.01 dB, and the ratio of passing with incoming signal was 39.76%. Compared with the case of the 5000 \(\upmu\)m, the modulus of S21 increased by 0.98 dB (about 12.2%). The signal transmission quality grew by 4.4%, and the corresponding frequency of the extreme point increased by 10.79 GHz.
# Human Health Impact Analysis of Contaminant in IoT-Enabled Water Distributed Networks
## 3 Materials and Methods
### Methods
#### 3.2.3 Health Impact Analysis  
Health impacts are measured in terms of the number of people exposed to or affected by the contaminated water. Potential public health impacts on contaminants can differ dramatically across networks. Public health impacts increase with the network population for contaminants with high toxicities, particularly for the scenarios with injection locations that have consequences near the upper end of the distribution. On the other hand, for less toxic contaminants potential public health impacts have not been shown to be sensitive to the network population, even those across the network that differ by more than a factor of population size. This is because contaminants with low toxicity appear to create localized impacts that are constrained by the mass of the available contaminant rather than the contaminants with high toxicity. The public health impact \(I_{ei}\) is analyzed and evaluated by calculating the mass of the substance to enter the body using Equation (5):  
\[I_{ei}i=\frac{q\sum_{t=0}^{t}C_{i}\times\Delta t}{G\times t} \tag{5}\]  
where \(q\) is the volume of consumed water and \(C_{i}\) is the contamination concentration. \(G\) is the average weight of the human body during the exposure, and \(t\) is the duration of exposure. The risk will be ignored if the value of \(I_{ei}\) is too small relative to the tolerable daily intake. The volume of consumed water is represented using Equation (6):  
\[q_{ne}(t)=\begin{cases}C_{ne}\times d_{i}(t)\times\Delta t&if\quad\quad C_{ ne}(t)\geq C_{min}\\ 0&\quad\quad\text{otherwise}\end{cases} \tag{6}\]where \(q_{ne}\) is the volume of the water at node n for event \(e\), \(C_{min}\) is the threshold hazard concentration, and \(C_{ne}\) is the concentration at node n for event \(e\). \(C_{ne}\) is the contaminant concentration, \(d_{i}\) (t) denotes the water demand at node \(i\) at a time step, and \(\Delta t\) represents the time step interval. The volume of contaminant water for all event scenarios is calculated based on Algorithm 1.  
```
1: Generate Contamination Scenarios (S).
2: Select one scenario (C) from S.
3: WHILE ( C \(\leq S\) )
4: Simulate Hydraulic and Quality models.
5: If (Contamination Concentration(CC)\(>>\)Threshold)
6: Contaminate water volume (q) \(\leftarrow\) cc \(\times\)\(\Delta t\)\(\times\) demand
```  
**Algorithm 1** Calculating the volume of the contaminant water  
The research methodology represented in Figure 2 can be summarized as follows:  
* Generate and simulate water contamination propagation on the pipeline networks (WDS).
* Develop the hydraulic and water quality models of the pipeline networks.
* Calculate dose level delivered to one section of the water network, which is supplying N people using Equation (1).
* Evaluate the health impact based on the calculation of the mass of the substance entering the body using Equation (5).
# Targeted Delivery Strategies for Hydrophilic Phytochemicals
## 3 Hydrophilic Phytochemicalemicals
### Flavonoids
#### 3.1.3 Acetamide and Sulfate Modifications  
Further chemical modifications, such as acetamide and sulfate substitutions, have been shown to enhance flavonoid hydrophilicity. Research by [PERSON] (2022) demonstrated that replacing hydroxyl groups with acetamide moieties in flavonoids like quercetin and luteolin significantly improves their solubility and pharmacokinetic profile while retaining antioxidant properties [111]. This structural adjustment not only increases polarity but also enhances molecular stability.  
Highly sulfated flavonoid derivatives, such as quercetin sulfate or (+)-catechin sulfate, exhibit extreme hydrophilicity due to their strong negative charge and elevated polarity [121]. These modifications, analyzed using capillary electrophoresis, provide enhanced solubility and biological stability in aqueous environments, offering potential for novel therapeutic applications [121].
## 2 Materials and Methods
### Meshing and BCs  
In order to reduce computational requirements, the problem is supposed to be axisymmetric. Therefore, only a thin slice of the winding with an angular extension of 4\({}^{\circ}\) was reproduced in the computational model (Figure 3a). As shown in Figure 3b, a block structured grid with non-uniform cell grading was used for meshing the domain, with non-conformal interfaces between fluid and solid regions, and with just one cell in the azimuthal direction, for a total cell count of more than 4.7 million cells. With respect to a previous work [17], a net saving of 15% of the cell count was achieved thanks to the adoption of non-conformal interfaces, without noticeably affecting the solution. The smallest cells (base cells) of the fluid zone, with a size \(\Delta\)c = 0.04 mm, are located at the sharp edges between the horizontal and vertical ducts, as shown in Figure 3b. Special attention was paid in controlling the mesh grading in horizontal ducts due to the possible onset of Rayleigh-Benard-like flow structures.  
A fixed inlet oil temperature of 46.7 \({}^{\circ}\)C was imposed for all the cases investigated in this work, as in [16]. At the two inlets of the entry region, a total mass flow rate of 0.78 kg/s was enforced by specifying the inlet bulk velocity. The mass flow rate, which is referred to a 360\({}^{\circ}\) cylindrical domain, was kept fixed for all the cases considered; therefore, slightly different inlet velocities were set from case to case to account for the appropriate density value at the inlet. An average pressure condition, \(\mathrm{p_{avg}}=0\), was imposed at the winding outlet. The no-slip condition was specified at all the fluid-solid boundaries, with all other solid walls set as adiabatic. The present model does not include the volume of oil below the first disc; hence, a constant heat transfer coefficient of 100 W m\({}^{-2}\) K\({}^{-1}\) was imposed on the bottom surface of the paper of the first disc to account for heat transfer toward the bottom oil, as in [16]. A uniform heat source was specified in the copper conductor region to reproduce the value of 676.9 W/disc, reported in [16], which is valid for a 360\({}^{\circ}\) domain, and corresponds to a heat flux of q\({}^{\prime\prime}\) = 2560 W m\({}^{-2}\) on the external surface of each disc. In the azimuthal direction, axial symmetry was imposed.  
Figure 2: Thermophysical properties as a function of temperature for the four considered oils: reference oil (blue lines), Nytro Taurus (green lines), Midel 7131 (magenta lines) and Midel 1215 (orange lines).  
Further details on the numerical model, including a grid convergence study and validation, can be found in [17].  
Figure 3: (**a**) Three-dimensional view of the entire computational domain; (**b**) detail of the fluid and solid domains of the first pass and entry region, with indication of the main geometrical dimensions and details of the mesh topology; (**c**) view of the upper part of the computational domain and mesh of the first pass.
# Some Critical Insights into the Synthesis and Applications of Hydrophobic Solid Catalysts
## 1 Introduction
This could limit the usage of heterogeneous catalyst in esterification, etherification, hydrolysis, (de)hydration and condensation reactions because they involve the use or the production of water. Moreover, it has to be considered that water is the best green solvent available especially when facing with biomass valorization reaction pathways [14]. The water tolerance of the catalyst increases with its hydrophobicity even if water is the solvent or a byproduct. The preferential adsorption or desorption of reactants and products (Figure 2) is a crucial point to consider especially if they exhibit different polarities. This is relevant in particular when water or very polar compounds are produced, as mentioned previously, or if water is present as an inert impurity in the feed. \begin{table}
\begin{tabular}{c c} \hline \hline
**Functional Group** & **Occurrence in Solids** \\ \hline -OH & Oxides, hydroxides \\ -COOH & Carbons \\ -C=O & Carbons \\ -O- & Oxides, carbons \\ Ionic species (i.e., H\({}^{+}\), Na\({}^{+}\), Mg\({}^{2+}\)) & Ion exchanger \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Functional groups that affects surface wettability of a solid [6]. Figure 1: Water contact angles for hydrophilic (**a**), hydrophobic (**b**) and superhydrophobic (**c**) surfaces. Moreover, the hydrophobicity of the surrounding species affects the process occurring at the catalytically active sites. For example, these can be more or less available depending on their closest proximity. Considering this aspect, hydrophobic catalysts could be an important tool in organic synthesis: They possess an elevated resistance towards water; therefore, catalyst life can be prolonged and the deactivation of active sites due to water molecules avoided. Besides, a wide number of organic substrates tend to show a low hydrophilicity; hence, hydrophobic surfaces should have a higher affinity with them, thus boosting the catalytic activity. Some reviews have already discussed the water tolerance related to the wettability of the catalysts [10; 12; 13]. This review has the purpose of showing the advantages and possibilities in the synthesis, characterization and application of hydrophobic catalysts in organic synthesis and in biomass vaporization. We focused our attention on three kind of solid materials, namely zeolites, mesoporous silica and polymers and on some selected reactions to underline the improvement of the catalytic activity thanks to the use of a hydrophobic catalyst in particular due to preferential adsorption of the reactants and desorption of products.
# A Theoretical Comparative Study of CO\({}_{2}\) Cascade Refrigeration Systems
## 4 Conclusions  
The objective of this work is the investigation of different cascade configurations with CO\({}_{2}\) in the low-temperature circuit and other refrigerants in the high-temperature circuit. The analysis is conducted with a developed model in EES, which is validated with literature data. The most important conclusions of this study are summarized below:  
* The COP of all the examined cascade systems is found to be higher than the reference scenario of the CO\({}_{2}\)/CO\({}_{2}\) cascade configuration. The enhancements with the other systems are found from 10% up to 80% and they are higher for higher heat rejection temperatures.
* The most efficient working fluids energetically, in the high-temperature circuit, are R152a, NH\({}_{3}\), R1270, R600, R600a and R290. The less efficient systems, except CO\({}_{2}\), are the refrigerants with high GWP such as R507A and R404a.
* The maximum mean yearly COP is found for R152a/CO\({}_{2}\) in all the evaporator temperatures and it is 2.381 for (\(T_{e}=-35\)\({}^{\circ}\)C), while the respective of CO\({}_{2}\)/CO\({}_{2}\) is 1.901.  
Figure 11: TEWI of four cases for different evaporator temperatures.  
* The environmental index TEWI shows that the CO\({}_{2}\)/CO\({}_{2}\) is not a so good choice with the value of 1659.2 for (\(T_{e}=-35\)\({}^{\circ}\)C), while the R1270 has 1326.9. The natural refrigerants, the R152a, R1234 yf and R1234 ze(E) have also relatively low TEWI. The reason for the high value in the CO\({}_{2}\)/CO\({}_{2}\) system is the high indirect TEWI.
* It can be said that there is an overall optimum case because the most efficient choices have flammability and toxicity issues. R152a seems to be a promising choice but has a GWP of 124. So, for the final selection of the high-temperature circuit refrigerant, extra parameters such as the refrigerant cost and the legislation have to be taken into consideration in every
## 2 Materials and Methods
### Analysis of Fermented Wort and Matured Beer
#### 2.5.5 Antioxidant Activity via 2,2-Diphenyl-1-picrylhydrazyl (DPPH) and  
2-2\({}^{\prime}\)-Azino-bis-3-ethylbenzothazoline-6-sulfonic Acid (ABTS) Assays  
The antioxidant activity of wort and beer were assayed by colorimetric methods. The 2,2-di-phenyl-1-picrylhydrazyl (DPPH) is a stable free-radical compound used for a radical scavenging capacity assay of hydrophobic compounds based on electron transfer activity,whereas ABTS is another form of free-radical compound used for an assay of hydrogen atom transfer by antioxidant compounds soluble in an aqueous and organic solvent.  
A DPPH solution of 80 uM was prepared in absolute methanol. A DPPH solution of 2.9 mL was mixed with a 100 uL sample (wort/fermented wort and beer), deionised water (blank), or standard Trolox. The mixture was then vortexed and incubated at room temperature for 60 min and kept in a dark cabinet. The absorbance values of the sample, a blank, and the standard reaction were taken at a wavelength of 517 nm using a Shimadzu UV-1800 spectrophotometer. A standard calibration curve of Trolox (0.02-0.08 mM) was plotted. The antioxidant activity of each sample was expressed as micromole of Trolox equivalent (TE) per litre (umol. TE L\({}^{-1}\)) [16].  
The ABTS assay was conducted according to [PERSON] et al. [16] and [PERSON] et al. [11]. About 10 mL ABTS solution (7 mM) was prepared containing 2.45 mM potassium persulphate. The mixture was kept at room temperature for 16 h in a dark cabinet prior to use. The 10 mL stock ABTS solution was mixed with reverse osmosis (RO) water to have a final absorbance of 0.700 \(\pm\) 0.02 at 734 nm using RO water as a reference. Antioxidant activity was determined using a similar DPPH reaction volume with an incubation time of 20 min. A Trolox (0.01-0.08 mM) calibration curve was constructed
# Building Realistic Mobility Models for Mobile Ad Hoc Networks
## 5 Battlefield Simulation Model
### Scenario Description and Implementation
#### 5.5.3 Communications Model  
The standard military organisation of a fire team of four led by a corporal is used to model the communications path requirements. Team members communicate to their corporal and corporals communicate to the commanding officer. Within the simulation, each node is numbered. Node_(0) is the platoon commander. Node_(1) is the corporal for the first fire team, which includes Node_(2), Node_(3), and Node_(4). Node_(5) is the corporal for the second fire team, which includes Node_(6), Node_(7), and Node_(8), and so on. Because members of a fire team operate in close proximity, there is no significant multi-hop routing within each fire team. As each fire team progresses through the building, multi-hop routes are required back to the commanding officer. As teams move relative to each other and the commanding officer, routes are broken, and new routes are established. In this experiment, soldiers communicate only to their corporal and only corporals transmit messages back to the commanding officer. Any node can act as an intermediate routing node. Constant bit rate traffic over user datagram protocol (CBR/UDP) is used. User data may comprise telemetry data, such as data derived from soldiers' vital signs monitoring and a set of standard signals, such as \"target secured\" and \"man down\". It is not necessary to provide high data rates for such information, so runs are performed with data packet intervals of one second and five seconds. A number of runs are conducted to determine the effect of variations in randomly generated traffic under these configurations.