esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Field Load Tests  
The damage to the target bridge was assessed via static and dynamic load tests. As shown in Figure 11, deflection gauges and accelerometers were installed to obtain the data for damage assessment. The deflection gauges were installed at a distance of 1.5 m from both ends of the deck on the undersides of spans S1 and S2; a total of four gauges were installed. The accelerometers were installed at the center of the curbs; two accelerometers were used in total. Figure 12 shows photographs of the actual deflection gauges and accelerometers installed on the bridge.  
Figure 11: Sensors installation of (**a**) span of slab 1, (**b**) span of slab 2 on the target bridge.  
Figure 12: Field installation of (**a**) deflection gauge, (**b**) accelerometer and (**c**) data logger on the target bridge.  
Similar to the structural analysis, the load was applied using a real vehicle, as shown in Figure 13. The load was applied to the central parts of both lanes and the middle section of the bridge. Static load tests were repeated twice for each load case to ensure the reliability of the test results. The deflection of spans at different driving speeds, namely 5, 20, 40 and 55 km/h, was measured during the dynamic load tests [13,14].
## 3 Results and Discussion
### Effects on the Production of Reactive Species
For instance, when the oxygen content is increased only the O777 emissions present a clear non-monotonical behavior, reaching a peak value at \(\sim\)0.6%. By contrast, the NO and OH emissions do not present such well-defined maximum, with an exception for the NO emission when the He flow rate is 2.5 L/min. In this case, when the additional O2 in the gas is close to 0.1-0.2% a small increase in NO emission is detected. Regarding the curves in Figure 5b obtained for a distance of 5 mm between the plasma outlet and the target, it can be seen that the measured emission intensities employing He flow rates of 1.5 L/min and 2.0 L/min change significantly for all emitting species. Significant differences in the intensity values for the O777 emission are measured across the entire range, while the emissions differences for both NO and OH are notable only when the additional O2 percentage is lower than 0.5% and, after that, the differences become smaller. Comparing the NO and OH emissions in Figure 5a,b a point that is worth highlighting is that in the second case, for a higher distance between the plasma outlet and the target, the NO emissions present a maximum value in its intensity when a very small amount of  
Figure 5: Intensities of selected lines/bands from O, NO, and OH radicals as a function of the additional O2 percentage for different He flow rates (indicated in the curve legends). The data were acquired for distances d = 3 mm in (**a**) and d = 5 mm in (**b**). Symbols represent experimental data and lines are trend curves. \(\mathrm{O_{2}}\) is added to the plasma. This behavior was predicted by some simulations performed using the global model for APPJs and verified in some experimental works [39]. On the other hand, the behavior of OH and NO emissions presented in Figure 5a, for the lower distance between the plasma outlet and the target, agrees with some results reported in the literature [15; 40]. Figure 6 shows the profiles of RONS as a function of the additional \(\mathrm{O_{2}}\) for different distances between the plasma outlet and the target. Regarding the intensity measurements of the light emitted by RONS for different \(\mathbf{d}\) values an important remark must be made. It is that as \(\mathbf{d}\) increases, different portions on the plasma are detected by both spectrometers, i.e., when \(\mathbf{d}\) = 3 mm, the Avantes and Horiba spectrometers see \(\sim\)83% and \(\sim\)53% of the plasma jet, respectively, while when \(\mathbf{d}\) = 7 mm, the first spectrometer sees \(\sim\)36% and the second one only \(\sim\)23%. It is interesting to notice that using the particular setup presented in this work, the peak values of the \(\mathrm{O_{777}}\) emissions obtained in all explored experimental conditions occurred for approximately 0.5-0.6% of \(\mathrm{O_{2}}\) content in the gas admixture. This finding coincides with the value where the highest efficiency in the production of O atoms was observed by [PERSON] et al. [15].
# Bioactive Compounds and Nanodelivery Perspectives for Treatment of Cardiovascular Diseases
## 3 Types of Bioactive Compounds
### Flavonoids  
Flavonoids are a copious and distinct cluster of bioactive compounds that occur as the core elements of polyphenols. They are divided into flavonoids, flavonols, flavanonols, flavanones, flavans (catechins, anthocyanins, and proanthocyanidins) and isoflav and flavononols [80]. Each flavonoid subclass and category have its own arrangement of herbal sources, roles, and therapeutic effects. As for their recognized antioxidant and anti-inflammatory effects, this arrangement of herbal bioactive compounds was revealed to have wellness benefits for individuals [81]. The main properties, source, and structure of flavonoids are briefly presented in Table 2, while a list containing the types of flavonoids, their functional unit, source, and therapeutic properties are presented in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Source** & **Bioactive Compounds** & **Therapeutic Potential** & **Ref.** \\ \hline
37. & Rice bran oil & Plant sterols, sitostanol, stigmaterol, campesterol. & Hypocholesterol and LDL & [74] \\ \hline
38. & Soybeans & Genistein, daidzein, isoflav. & Reduced LDL-cholesterol level. & [75] \\ \hline
39. & Thai Perilla frutescens & Cyanidins, luteolin, phenolic acids. & Anti-inflammatory, antioxidant. & [76] \\ \hline
40. & Tomato & Phenols: phenolic acid, flavonoids, carotenoids. & Antioxidant, anti-inflammatory, antiplatelet, antihypertensive. & [77] \\ \hline
41. & Trichosanthes kirilowii & Luteolin. & Hypolipidemic, antioxidant, antiatherosclerotic. & [78] \\ \hline
42. & Wild rice & Phytic acid, luteolin glycoside, _p_-hydroxy acetophenone glycoside, 3,4,5-trimethoxycinnamin acid. & Health-promoting. & [79] \\ \hline \hline \end{tabular}
\end{table}
Table 1: \(Cont.\)  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Flavonoids with Main Features** & **Natural Sources** & **Bountiful Health Benefits** \\ \hline - & Flavonoids are hydrophilic. It has a & C6–C3–C6 backbone. It consists a \\  & 15-carbon skeleton (two benzene rings \\  & linked with three-carbon chain i.e., \\  & oxygenated ring). & It is produced in response to microbial \\  & infection by plants. & The antioxidant activity is dependent on \\  & arrangement of functional groups. & Flavonoid basic structure [82; 83]. \\ \hline \hline \end{tabular}
\begin{tabular}{c c} \hline \hline  & **Berries, spices, leeks, ginger, herbs, graprefruit, carrot, grapes, apple, onion, broccoli, cabbage, kale, tomato, lemon, parsley, buckwheat, legumes, coffee, tea.** \\ \hline - & Inoflavonoids basic structure. & Berries, spices, leeks, ginger, herbs, graprefruit, carrot, grapes, apple, onion, broccoli, cabbage, - & Rich in phytontutrients, antioxidant. \\  & - & Neutralize free radicals. \\  & - & Limit cells damage. \\  & - & Anti-inflammatory, anti-aging properties. \\  & - & Improve the quality of \\  & blood vessels [84]. \\ \hline \hline \end{tabular}
\end{table}
Table 2: The main properties, source, and health benefits of flavonoids.
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## Part I Introduction  
As the technology evolves, the wireless sensors manufactured become technically more powerful and economically viable. In wireless sensor networks (WSN) each node consists basically of units for sensing, processing, radio transmission, position finding and sometimes mobilizers [1]. These sensors measure desired phenomenal conditions in their surroundings and digitize them to process the received signals to reveal some characteristics of the conditions in the surrounding area. A large number of these sensors can be networked in many applications that require unattended operations, hence producing a WSN. In general, the sensor nodes in a wireless sensor network WSN sense and gather data from surrounding environment and transmit it to one or more sinks, to perform more intensive processing. The number of applications for WSNs is large, many of these are in the fields of weather monitoring, surveillance, health care, detecting ambient conditions like temperature, sound, light, security related aspects, etc. More fields are deploying WSNs as their reliability, performance and capabilities keep getting even better and wider. In many applications, replacement of damaged or energy depleted nodes is not possible. Moreover planned nodal placement may not be a possible thing to do. Therefore, two of the main requirements for WSNs to operate reliably are to consume the minimum amount of energy to prolong the network's life time, and to be able to self organize themselves when the network topology changes. Other requirements (e.g., limited delay, good signal to noise ratio, etc.) are usually application specific. Moreover, there are differences in the nature of WSNs. For example, there could be WSNs with either rechargeable or non-rechargeable sensor batteries, either single sink WSNs or multiple sink WSNs, which could either be immobile or mobile. Depending on these different variants of WSNs, different types of applications and the traffic types they handle, different design considerations will need to be taken into account. An acoustic WSN, for example, would need to consider propagation delays in any design aspect which most likely will be ignored in radio based WSNs. Also, as another example if voice or sound is to be sensed and transmitted for surveillance applications, then strict delay requirements for routing the packets will have to be enforced. Besides the application types and the WSN variants, there are many aspects such as routing, clustering, accurately estimating sensed data, visual target tracking etc. in which optimal design is needed. In all these design problems, energy is a common limitation for the reasons mentioned earlier. For this paper we selected key optimization aspects of design problems relating to WSNs, carried out a thorough review of the selected major papers and used that opportunity to present a tutorial on the subject. The papers selected for this review and tutorial are based on how well they covered the topics of interest and the degree to which they highlight the key design issues for WSN.
## 2 Theory of Tunnel Deformation under Surcharge Loading
### Responses of Tunnels to External Loads  
The load-tunnel calculation model is shown in Figure 3. The existing tunnel is equivalent to a continuous Euler-Bernoulli beam placed on a Pasternak foundation. The method assumes that the tunnel is always in contact with the soil and that no slip occurs at the tunnel-soil interface in any direction. In addition, the soil is assumed to be an isotropic elastic material, and the plastic deformation of the soil is not considered. The longitudinal deformation differential equation of the existing shield tunnel based on a Pasternak foundation subjected to surface surcharge stress is as follows:  
\[(EI)_{eq}\frac{\mathrm{d}^{4}w}{\mathrm{d}x^{4}}-G_{c}D_{t}\frac{\mathrm{d}^ {2}w}{\mathrm{d}x^{2}}+kD_{t}w=q(x)D_{t} \tag{4}\]  
where \(w\) is the longitudinal deformation of the tunnel, and \(q(x)\) is the external load acting on the tunnel. \(D_{t}\) is the tunnel diameter, and \(\left(EI\right)_{eq}\) is the equivalent bending stiffness of the tunnel. Equation (4) is a high-order ordinary differential equation, which is not convenient to solve directly. In the proposed approach, the solution is determined using the finite difference method. First, as shown in Figure 6, the finite difference method is carried out for tunnels and other elements. The tunnel is divided into \(n+5\) units in equal proportion, of which 4 virtual units are located at the two ends of the tunnel (2 at each end), as shown in Figure 2. The unit length is \(l\). According to the finite difference principle, the finite difference form of Equation (4) is  
\[\left(EI\right)_{eq}\frac{6w_{i-4}(w_{i+1}+w_{i-1})+(w_{i+2}+w_{i-2})}{l^{4}}+ kD_{t}w_{i}- \tag{5}\]  
where \(w_{i-2}\), \(w_{i-1}\), \(w_{i}\), \(w_{i+1}\), and \(w_{i+2}\) are the longitudinal displacements of the tunnel elements \(i-2\), \(i-1\), \(i\), \(i+1\), and \(i+2\), respectively, and \(q(x_{i})\) is the external load acting on the units. Figure 5: Coordinates between surface surcharge and the existing tunnel. For the boundary conditions of the existing tunnel, it is assumed that both ends of the tunnel are free.
## WEAMR -- A Weighted Energy Aware Multipath Reliable Routing Mechanism for Hotline-Based WSNs  
**[PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] ***  
Graduate School of Information and Communication, Ajou University, Suwon 443-749, Korea;  
E-Mails: [EMAIL_ADDRESS] (A.T.); [EMAIL_ADDRESS] (A.Q.); [EMAIL_ADDRESS] (A.M.K.); [EMAIL_ADDRESS] (W.A.B.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS].  
_Received: 12 April 2013; in revised form: 8 May 2013 / Accepted: 8 May 2013 / Published: 13 May 2013_  
**Abstract:** Reliable source to sink communication is the most important factor for an efficient routing protocol especially in domains of military, healthcare and disaster recovery applications. We present weighted energy aware multipath reliable routing (WEAMR), a novel energy aware multipath routing protocol which utilizes hotline-assisted routing to meet such requirements for mission critical applications. The protocol reduces the number of average hops from source to destination and provides unmatched reliability as compared to well known reactive _ad hoc_ protocols _i.e._, AODV and AOMDV. Our protocol makes efficient use of network paths based on weighted cost calculation and intelligently selects the best possible paths for data transmissions. The path cost calculation considers end to end number of hops, latency and minimum energy node value in the path. In case of path failure path recalculation is done efficiently with minimum latency and control packets overhead. Our evaluation shows that our proposal provides better end-to-end delivery with less routing overhead and higher packet delivery success ratio compared to AODV and AOMDV. The use of multipath also increases overall life time of WSN network using optimum energy available paths between sender and receiver in WDNs.  
**Keywords:** wireless sensor network (WSN); sensor nodes; reliability; backbone routers
## 5 Shaking Table Test
### Input Seismic Motion  
The Required Response Spectrum (RRS) for the input seismic motion used in shaking table testing was developed based on the seismic design criteria, which are more stringent than the KDS criteria applied in the analytical study. The RRS was created by referencing AC 156 with an amplification of 150%, following common application criteria for seismic design. The reason for applying more stringent seismic performance criteria is to experimentally validate the aspects predicted through the analytical results using methods such as shaking table testing, to ensure practical verification. The acceleration (\(S_{DS}\)) for the development of the RRS was calculated according to Equations (4) and (5):  
\[S=Z\times I \tag{4}\]  
\[S_{DS}=S\times 2.5\times F_{a} \tag{5}\]  
The seismic zone factor (\(Z\)) for the determination of effective horizontal ground acceleration (\(S\)) was assumed to be 0.11 g, encompassing the majority of domestic regions. The hydrogen storage vessel, the subject of the test, was classified as earthquake-resistant special grade due to its potential significant impact on society in the event of a seismic disaster and substantial functional impairment. Therefore, with a return period of 2400 years, the hazard coefficient (\(l\)) was assumed to be 2.0, resulting in \(S\) = 0.22 g. The short-period ground amplification factor (\(F_{a}\)) was assumed to be 1.5 according to KDS, considering the assumption of soft soil and effective horizontal ground acceleration. Therefore, \(S_{DS}\) = 0.825 g. The calculated spectral displacement (\(S_{DS}\)) was used to present the RRS and seismic parameters according to AC 156 in Figure 14 and Table 4. The Test Response Spectrum (TRS) was required to adhere to the Required Response Spectrum (RRS) within the allowable range of \(-\)10% to +50% of the RRS. If the TRS exceeded +50% of the RRS, approval from the verification authority was required.  
The acceleration time histories for the seismic simulation test were generated based on the developed RRS, and the damping ratio was set to 5%. Referring to IEEE 344, the correlation function values were adjusted to be below 0.3, and the vibration duration and earthquake duration were set to 30 s and 20 s, respectively, within the frequency range of 0.5 Hz to 50 Hz.  
Figure 14: Required Response Spectrum (RRS) and Test Response Spectrum (TRS) based on ICC-ES AC 156 with Amp. 150%.
# Rank-Based Ant System with Originality Reinforcement and Pheromone Smoothing
### Aims and Structure of This Paper  
In this work, we aim at improving the performance of AS\({}_{Rank}\) algorithm for combinatorial optimization problems. Based on the observations described in the previous paragraphs, this paper proposes an extended version of AS\({}_{Rank}\), which includes two diversification strategies:  
1. an originality reinforcement strategy that rewards the originality (dissimilarity from already searched space) of the solutions with good fitness; and
2. a pheromone smoothing mechanism that is triggered before the algorithm reaches stagnation, increasing exploration and making possible it to find better solutions.  
This new ACO algorithm, called AS\({}_{ORank}^{ps}\), will be compared with the state-of-the-art ACO algorithms using several instances of the popular TSPLIB95 benchmark. Since our main interest is at improving the state-of-the-art ACO algorithms, in this paper we will focus our comparative work on the best ACO algorithms exclusively. Consequently, we will restrict our analysis to ACO algorithms without any addition of external methods, such as local search procedures or hybridization with other metaheuristics. These procedures, although very promising and potentially successful, would obscure the understanding of the behavior of our algorithm with technicalities totally unrelated to the core and principles of the ACO algorithms. The discussion about how to enhance our new ACO algorithm with local search strategies and/or hybridization with other metaheuristics is out of the scope of this paper and will be part of our future work in the field (see our discussion in Section 5 for further details).  
The remainder of the paper is organized as follows. Section 2 introduces the ACO metaheuristics, describing the most important ACO algorithms and presenting an overview of the current research lines in ACO state-of-the-art. Section 3 describes the proposed Rank-based Ant System with originality reinforcement and pheromone smoothing. Section 4 analyses the performance of the proposed ACO algorithm over several TSP and Sequential Ordering Problem (SOP) instances, and compares it with the best-performer ACO algorithms in the literature. Finally, Section 5 summarizes the main conclusions of the work and some futures lines of research.  
Figure 1: Evolutionary curves of ACO algorithms (AS, AS\({}_{Rank}\), MMAS and ACS) for TSP instance _gr48_.
# Hierarchical Transmission Tower Detection from High-Resolution SAR Image
## 2 Methodology
### Potential Transmission Tower Pixels Detection
where \(f(\cdot)\) is the probability density function (pdf) of the Gaussian mixture model, \(\Phi_{k}\) is the weight, \(\mu_{k}\) and \(\sigma_{k}\) are the mean and variance of \(k\)th class, \(\Phi_{k}\), \(\mu_{k}\) and \(\sigma_{k}\) can be calculated by Expectation-Maximization (EM) algorithm. The threshold is chosen according to the Gaussian mixture model distribution at the wave crest corresponding to the maximum and next largest \(\mu_{k}\). Without loss of generality, \(\mu_{k}\)'s, \(k=1\),..., \(K\), are arranged in ascending order. Thus, it can be said that the SCR threshold is between \(K\)-1 and \(K\) (\(K>2\)) means. To be accurate, SCR threshold can be calculated as follows,  
\[T_{scr}=\left\{ \begin{array}{ll}\arg\Bigl{\{}\int_{0}^{SCR}f(SCR,\mu,\sigma )dSCR=0.9\Bigr{\}},\text{ if }K=1\\ \arg\bigl{\{}f(SCR,\mu_{K-1},\sigma_{K-1})=f(SCR,\mu_{K},\sigma_{K})\bigr{\}},\text{ if }K>1\end{array} \right. \tag{3}\]  
As a result, the set of indexes of potential transmission tower pixels can be obtained as \(\mathbf{P}=\{i,SCR_{i}\geq T_{scr}\}\).
# Progress of Process Monitoring for the Multi-Mode Process: A Review
## 4 Steady-State Mode Process Monitoring Methods
### Supervised Process Monitoring Method
#### 4.1.1 Expert System Method  
The expert system method is a qualitative method that uses human experts to observe the system state, qualitatively evaluates the system state using expert knowledge of the system and faults, and infers the faults that may occur in the system. This method is simple to model but suffers from uncertainty in describing the results and entails consumption of much expert energy. The basic purpose of the expert system method is to establish a knowledge base from engineering experience accumulated by experts over a long period, to design a set of computer programs to simulate the thinking and judgment of experts, and to infer process data information according to the knowledge base. Expert system methods can be classified into traditional expert systems, fuzzy expert systems and belief rule-based expert systems. [PERSON] et al. [16] proposed a multi-mode process monitoring strategy based on an expert system that formulated if-then rules from the knowledge base to enable the identification and monitoring of each mode.
# Formation Control Algorithm of Multi-UAV-Based Network Infrastructure
## 4 Formation Control Algorithm
### Feedback-Based Control  
Through the forces defined in Sections 4.1-4.3, we designed a novel feedback-based UAV formation control algorithm as Algorithm 1. In addition to the summation of the forces, we added the ad hoc network expansion procedure named diffusion from line 7 to 13. At the first step, all UAVs are directly connected to the GCS with a single hop to sustain the connectivity of the network infrastructure. At the state of the line 7, a UAV could guarantee the route path to the GCS by one of its neighbors in \(E\). Then, the UAV cuts the farthest next hop connection and declares the diffusion to the nearest neighbor, to prevent the loss of the GCS connections of all UAVs. NeighborDiffused() function returns true if the UAV is informed the diffusion of another UAV.  
```
1:Collect \(\mathbf{p_{0}}\)
2:Initialize \(H=\{\mathbf{p_{0}}\},E=\phi\)
3:while True do
4: Refresh \(\mathbf{p},\mathbf{p_{i}},n,n_{i},E\)
5:d\(+\)\(-\)\((0,0,0)\)
6: Calculate \(F_{adhoc}\), \(F_{dist}\), and \(F_{coneq}\)
7:if\(F_{adhoc}\
eq 0\) and \(|F_{dist}|+|F_{coneq}|>|F_{adhoc}|\)then
8:if\(\mathtt{INeighborDiffused()}\)then
9: Remove \(\arg\max_{j}|\mathbf{p}-\mathbf{p}|\) from \(H\)
10:\(H\gets H\cup E\)
11: Recalculate \(F_{adhoc}\)
12: Inform the diffusion to the nearest neighbor
13:endif
14:endif
15:d\(\gets F_{adhoc}+F_{dist}+F_{coneq}\)
16: Set direction to \(\frac{\mathbf{d}}{|\mathbf{d}|}\)
17:endwhile
```  
**Algorithm 1** Formation control algorithm  
On the congestion equalization, the force applied by a few differences of the connection counts could lead to the oscillation of the UAV. Since the frequent AP change of the mobile device could lead to the degradation of the performance, we utilize the concurrent transmission availability \(\alpha\) as a threshold of the \(F_{coneq}\) summation. The larger value of the \(\alpha\) grants more sustainability of the UAV's formation.