esa-sceva/satcom-chunk-collection huggingface dataset:
# State-of-the-Art Review of Structural Vibration Control: Overview and Research Gaps
## 5 Control Algorithms Used for Different Semi-Active Control Systems
### Classification of Control Algorithms
The effectiveness of a specific controller can vary for different structures, indicating that the performance of control algorithms relies on the characteristics of the structural system and the earthquake data it experiences. It is crucial to compare structural responses using ground motions with the same hazard level. For example, the performance of a controller on the first floor may decrease as the number of building stories increases, making it more suitable for lower floors. Therefore, it is crucial to investigate the optimal number and placement of dampers based on the specific structures. To achieve a more robust performance evaluation for semi-actively controlled structures using MR dampers, it is necessary to expand the dataset by including more seismic ground motions, building more comprehensive models, and developing more advanced control algorithms. The most effective way to demonstrate the robustness of the control algorithms is through experimental testing. The review shows that several novel control algorithms have been developed to enhance the efficiency of the control systems [117, 158, 159, 160, 161, 162, 163, 164, 165, 166]. However, most of them are not tested experimentally. Experimental testing of semi-active control systems in structures requires addressing specific aspects to achieve a more realistic evaluation of their effectiveness in controlling earthquake-induced vibrations [174]. The practicality of a semi-active control system in mitigating the seismic impact on structures is studied through experimental testing at both the component level and with small-scale model structures affixed to a shake table [165]. It has been observed from the literature that control systems are developed to address specific challenges, such as uncertainty, time delay, and sensor optimization. The following section reviews the controllers developed to address specific issues.
## 2 Materials and Methods
### Damage-Sensitive Features
#### 2.1.1 Transmissibility-Based Features
The vector colinearity is linked to the modal assurance criterion (see Equation (4)), also known as cosine similarity, which is a common metric to assess the similarity between two modal shapes in OMA [74]:  
\[TAC_{io,m}=1-\frac{\left|T^{\prime}_{io}(\mathbf{\omega_{m}})^{T}T^{e}_{io}(\mathbf{ \omega_{m}})\right|^{2}}{\left|T^{\prime}_{io}(\mathbf{\omega_{m}})^{T}T^{e}_{io} (\mathbf{\omega_{m}})\right|\left|T^{e}_{io}(\mathbf{\omega_{m}})^{T}T^{e}_{io}(\mathbf{ \omega_{m}})\right|}\, \tag{4}\]  
where the superscript \(r\) refers to the baseline _reference_ signal, when the structure is characterized by its original (linear) stiffness, and \(e\) denotes an _earthquake response_ signal, with \(i\) and \(o\) once again denoting the input channel and output channels. In typical applications of seismic SHM, the reference signal is measured under ambient conditions, which can be considered to correspond to an excitation of flat frequency content (white noise). The values of \(TAC_{io,m}\) range between 0, indicating perfect colinearity between two transmissibility vectors and thus, absence of nonlinearity or damage, and 1, when the two transmissibility vectors \(T^{\prime}_{io}\) and \(T^{e}_{io}\) are perpendicular. The normalized values of the \(TAC_{io,m}\) facilitate an intuitive understanding of the corresponding DI. All three DSFs, whose primary ingredient is transmissibility, depend on a frequency bandwidth (\(\mathbf{\omega_{m}}\)), which needs to be defined. The first two indicators, \(Tmax_{io,m}\) and \(Tcnt_{io,m}\), depend on frequencies that maximize the transmissibility and thus should be restricted to a smaller bandwidth around individual peaks of the transmissibility in order to avoid sensitivity to changes in the input signal. The colinearity metric that defines \(TAC_{io,m}\) provides some robustness with respect to the relative height of multiple modes and thus can be defined across wider frequency bandwidths.
# Boosting Safety Protocol Compliance with Eye-Tracking in Chemical Plants
## 2 Materials and Methods
### Experiment Environment  
The experiment was conducted using a virtual reality (VR)-based chemical safety training platform designed to simulate real-world industrial operations with integrated eye-tracking technology. The system was developed using the HTC Vive Pro Eye VR headset (The HTC Vive Pro Eye VR headset is manufactured by HTC Corporation, headquartered in Taoyuan City, Taiwan, China.), which provided an immersive and interactive training environment. The platform was designed to replicate critical operational tasks, including raw material inspection, material mixing, and anomaly handling, ensuring realistic training conditions for participants. The VR environment facilitated hands-on experience in high-risk chemical production scenarios, allowing for controlled exposure to potential hazards without real-world consequences.
# Regulation of the _Pseudomonas syringae_ Type III Secretion System by Host Environment Signals
## 2 Environmental Regulation of the _P. syringae_ T3 SS
### Negative Regulation of T3 SS Gene Expression by Plant-Derived Compounds  
Plant-derived compounds capable of inhibiting T3 SS expression by _P. syringae_ have also been identified. One such T3 SS inhibitor is auxin, or indole-3-acetic acid (IAA), a plant hormone that is also synthesized by _P. syringae_[87; 88; 89]. Auxin suppresses T3 SS-associated gene expression in _P. syringae_, while stimulating the expression of genes encoding the type IV secretion system (T6 SS), which is involved in microbial competition [88; 89]. As IAA accumulates in _P. syringae_-infected leaves [90; 91; 92], T3 SS suppression by IAA may enable _P. syringae_ to taper T3 SS production in the advanced stages of infection after host immune defenses have been sufficiently disarmed and bacterial growth established in the apoplast [88]. In this way, _P. syringae_ may maximize its fitness by shifting cellular resources from the production of the T3 SS to the T6 SS during the later stages of infection, due to increased bacterial density in the apoplast and thus greater potential for microbial competition [88; 93]. The expression of T3 SS-encoding genes in _P. syringae_ pv. tomato DC3000 is also inhibited by sulforaphane, a defense-associated glucosinolate produced in Arabidopsis leaves and in other Brassicaceae [94]. Sulforaphane suppresses T3 SS gene expression through direct covalent modification of T3 SS master regulator HrpS. Arabidopsis plants deficient in glucosinolate production exhibited increased susceptibility to _P. syringae_ infection, suggesting that T3 SS inhibition by sulforaphane may be an important layer of host defenses [94]. Other plant-derived compounds including plant flavonoids and other phenolics [95; 96; 97], as well as a variety of synthetic compounds [98; 99], have also exhibited inhibitory activity towards the _P. syringae_ T3 SS.
# Technique Evolutions for Microorganism Detection in Complex Samples: A Review
## 3 Developments in Innovative Detection Methods
### Improvements and Developments in Real-Time and Online Analysis Techniques
#### 3.4.1 Bio-Conjugated Nanoparticles  
Conjugating nanoparticles (NPs) with ligands such as antibodies (see Section 2.1), make them interesting tags for pathogens detection [113]. [PERSON] et al. demonstrated the use of bio-conjugated NP as fluorescent tag in bioassays for the detection of bacteria [114]. Silica NPs were mixed with the sample to be analyzed and bounded to presumed bacteria thanks to specific antibodies (Figure 8). Then, unbound NPs were eliminated by centrifugation, and finally, the fluorescence of the sample was measured either in well plates by a fluorometer or by flow cytometry. As the NPs encapsulated a thousand fluorophores, they provided a fluorescence intensity a thousand times greater than what a single antibody-conjugated fluorophore could provide. Tested on _E. coli_, _Salmonella_, or _Bacillus cereus_ in ground beef samples suspended in a 10 mL solution, the high fluorescence signal allowed for the detection of a single bacterium under 20 min. Moreover, the measurement was quantitative. Their method also works for DNA detection.  
NPs can also be used as SERS tags for pathogen detection, as [PERSON] et al. showed with Dengue and West Nile viruses [115]. Conjugating NPs with selective antibodies, they reached an LOD of 10 plaque-forming units/mL in less than 30 min. Using magnetic bio-conjugated NPs, it is also possible to selectively isolate bacteria or aptamers specifics to bacteria [102].  
Figure 8: Drawing of bacterial cells with nanoparticles conjugated with antibody. The fluorescence intensity is strong, enabling single-bacterium cell identification in aqueous solution.  
In conclusion, bio-conjugated nanoparticles, combined with various detection systems, allow sensitive and specific pathogen detection. They can be used for real-time detection in industrial cell bioreactors or in the manufacture of innovative therapy drugs. However, the question arises as to whether the nanoparticles are safe for humans or cells, and the quantity required for high-volume applications allowing rapid detection of several bacterial species.
## 8 Numerical Results and Discussion
### Convergence Time Analysis of the RNN Framework
However, the convergence epochs, which represent the number of iterations required for optimization, are independent of the hardware and generalize to other systems. These results are therefore applicable to different hardware configurations, provided that similar RNN structures and VLC system parameters are used. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Configuration** & **Convergence Epochs** & **Convergence Time (s)** & **Max. SNR (dB)** \\ \hline Pyramidal ADR with MRC & 40 & 4.23 & 105.94 \\ Hemispherical ADR with MRC & 50 & 5.12 & 105.94 \\ Pyramidal ADR with SBC & 60 & 6.64 & 91.28 \\ Hemispherical ADR with SBC & 60 & 7.01 & 91.28 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Convergence time analysis for different ADR configurations.
## 2 Materials and Methods
### Analysis of Phenolic Composition
#### 2.5.2 HPLC-DAD-ESI-MS/MS  
Halophyte extracts were analyzed in a high-performance liquid chromatography (HPLC) system (Waters Alliance 2695, Milford, MA, USA), coupled to a diode array (DAD) detector (Detector Waters 2996, Milford, MA, USA), and outfitted with electrospray ionization source (ESI) and a triple quadrupole mass spectrometer (MS/MS) (Micromass, Waters) [11]. The HPLC system contained an autosampler, solvent degasser, quaternary pump, a pre-column (100 RP-18, 5 mm), and a reversed-phase C18 column (LiCrospher 100 RP-18, 250 \(\times\) 4 mm; 5 mm). Additionally, a thermostatic oven at 35 \({}^{\circ}\)C was used for the compounds' separation. The mobile phase consisted of water-formic acid (99.5%:0.5%) as eluent A and acetonitrile-formic acid (99.5%:0.5%) as eluent B at a flow rate of 0.30 mL/min. All solvents were filtered through a 0.22 mm PVDF membrane (Millipore, Billerica, MA, USA) before analyses. The gradient elution program was as follows: 0-10 min from 99 to 95% A; 10-30 min from 95 to 82% A; 30-44 min from 82 to 64% A; 44-64 min at 64% A; 64-90 min from 64 to 10% A; 90-100 min at 10% A; 100-101 min from 10 to 95% A; 101-120 min at 95% A; and finally returning to the initial conditions. The auto sampler's temperature was set at 7 \({}^{\circ}\)C, and the injection volume was 20 \(\mu\)L. DAD was used to scan the wavelength absorption from 200 to 650 nm. Tandem mass spectrometry (MS/MS) detection was carried out using an electrospray ionization source (ESI) at 120 \({}^{\circ}\)C and applying a capillary voltage of 2.5 kV and cone voltage of 30 V. The compounds were ionized in the negative mode and spectra were recorded in the range of \(m/z\) 60-1500. Analytical conditions were optimized to maximize the precursor ion signal ([M\(-\)H]\({}^{-}\)). Ultra-high purity argon (Ar) was used as a collision gas. High-purity nitrogen (N\({}_{2}\)) was used both as a drying gas and nebulizing gas. The data acquisition and processing were accomplished using MassLynx software (version 4.1, Waters, Milford, MA, EUA).
## 2 Adiabatic Nature of Exciton Self-Localization
Thus, the final state of the Franck-Condon (FC) excitation by light (the red vertical upward arrows), is this plane wave state, being the lowest one within the exciton band, as shown in Figure 1. While, after the lattice relaxation, as schematically shown by the dashed red allows in Figure 1, the whole system reaches the STE state. We should also note that, at this largely displaced lattice configuration, even the elastic energy of the ground state, as well as that of the STE,  
Figure 1: The adiabatic potential energy surface of an exciton, at the Franck-Condon excited state (the red upward vertical arrows), and the self-localized exciton (STE), as a function of the \(Q_{0}\). (**a**) The Luminescent case. (**b**) The photo-induced structural phase transition (PISPT) case. increases upto \(\omega_{0}\mathrm{S}^{2}/2\), since the lattice distortion (the last term of Equation (1)) is common to all states. If the total energy of this STE state is above the ground state one at this lattice configuration,  
\[\left(\mathrm{E}_{\mathrm{g}}+6\mathrm{T}_{\mathrm{F}}\right)-\frac{\omega_{0} \mathrm{S}^{2}}{2}>\frac{\omega_{0}\mathrm{S}^{2}}{2}, \tag{4}\]  
this STE state still remains in the gap of this insulating crystal, and finally disappears with a luminescence, of which energy is a little smaller that the exciting one, as shown in Figure 1a. This is the ordinary situation widely realized in luminescent insulators [8]. As shown in Figure 1b, however, if the exciton-phonon coupling is so large as to relax down even lower than the ground state at this largely displaced lattice configuration,  
\[\left(\mathrm{E}_{\mathrm{g}}+6\mathrm{T}_{\mathrm{F}}\right)-\frac{\omega_{0 }\mathrm{S}^{2}}{2}<\frac{\omega_{0}\mathrm{S}^{2}}{2} \tag{5}\]  
the system becomes non-luminescent, and the STE remains forever within the adiabatic approximation at absolute zero temperature. This is nothing but the start of the PISPT [10], although the domain size of the new phase is the possible minimum. Thus, we have seen the spontaneous translational symmetry breakage. Similar to the above Stoner theory [2], its mechanism is also a sufficient energy lowering from the perfectly symmetric state. According to the adiabatic principle, however, the speed of this symmetry breaking transition is also infinitely slow. Incidentally, within the framework of the present theory, we can formally encounter an extremely strong coupling case that the energy of the STE becomes even lower than the starting ground state itself; \(\left(\mathrm{E}_{\mathrm{g}}+6\mathrm{T}_{\mathrm{F}}-\omega_{0}\mathrm{S}^{ 2}/2\right)<0\). We cannot use Equation (1) for such a contradicting case.
## 2 Starch Modification Processes
### Chemical Modifications
#### 2.4.4. Esterification  
Esterification is the substitution of hydrophilic hydroxyl groups of starch by various hydrophobic groups, as shown in Figure 5. The degree of substitution is the number of hydroxyl groups replaced by the substituted groups per monomer unit [90], and the quality of the modified starch depends on the degree of substitution. The degree of substitution (DS) depends on the concentration of reagent, type of the reagent, catalyst, and duration of the reaction time. This method can be used to reduce the water absorption capacity of the starch. In starch, esterification weakens the intermolecular bonding that holds the granules together. As a result, it alters the granule shape, sizes, and other functional properties of the starch. The main types of esterification reactions are acetylation, succinylation, and phosphorylation. Among those, the acetylation reaction is widely used for esterification.  
In acetylation, the starch reacts with acetic anhydride to produce acetylated starch. The hydroxyl group of the d-glucose units is esterified with the acetyl groups, incorporating acetate functionality [1]. Acetylation enhances the hydrophobicity of starch. It also increases the thermoplasticity, thermal stability, and film-forming ability of starch [87]. The acetate groups act as pegs on the amylopectin chains to prevent retrogradation. Films prepared from acetylated amylopectin films show reduced elongation at break. However, acetylation causes an increase in the crystallinity of amylopectin by densely packing the amylopectin branched structure [91]. The elasticity of starch acetyl films can be improved using external plasticizers such as glycerol [92].
###### Abstract  
To find the optimal design for an engineering object, thousands of (or even more) simulations should be implemented to obtain the outcome data for the variously designed objects. However, repeating simulations this many times is impossible because a typical simulation is a computationally expensive task. Instead of conducting all the required simulations, a more efficient way is predicting the outcome from the approximation model, called the surrogate model. The response surface method (RSM) with polynomials and artificial neural network (ANN) are the most prominent methods in constructing a surrogate model in the engineering fields. In this study, the prediction accuracy of the surrogate models computed by using an RSM and ANN is compared with several datasets showing different complexities. This comparison is investigated by constructing the surrogate models in predicting aerodynamic performance of a wind turbine airfoil. In the current paper, it is verified that the prediction accuracy of the ANN-computed surrogate model is higher than the RSM-computed one when the datasets have a high level of complexity, but the opposite phenomenon is observed if the datasets have a low level of complexity. When the surrogate models with different accuracies are used to enhance the performance of a wind turbine airfoil, the surrogate model with a high level of accuracy produces the optimal design, showing a high performance improvement. The current study is expected to give guidance on how to properly choose between an RSM and ANN to construct a highly accurate surrogate model that can help in finding a design with a high performance improvement during the optimization process.  
wind turbine airfoil; aerodynamic performance; surrogate models; response surface method; artificial neural network; optimization +
Footnote †: journal: Applied Physics Letters A
## 4 Simulations
### BER Performance of Optimal Receiver  
Next, the best bit error performance of the noncoherent PPM receiver is numerically simulated, and the binary PPM, 4-ary PPM and 8-ary PPM are included for comparison.  
Figure 5: BER performance of noncoherent PPM with different threshold coefficients.  
Figure 6: BER performance of noncoherent PPM suboptimal receiver.  
The value range of \(E_{b}/N_{0}\) is 0-12 dB, and the BER is counted as \(10^{-4}\). The simulation results are shown in Figure 7.  
The theoretical curve in Figure 7 is calculated by the theoretical formula derived in Section 3.2. The simulation curve is the numerical simulation result. The simulation process first randomly generates the initial binary bit stream A, modulates it into a PPM signal, then sends it to AWGN channel and then restores it to binary bit stream B through the optimal receiver in this paper. We compare the similarities and differences between B and A and calculate the BER to obtain the simulation BER curve simulation.  
It can be seen from Figure 7 that the numerical simulation results are in good agreement with the theoretical formula derived in Section 3.2, indicating that the noncoherent optimal receiver bit error theoretical formula derived in this paper can be applied to a \(Q\)-ary PPM system, including binary PPM. It can also be seen that the larger the \(Q\), the better the BER performance of the noncoherent PPM receiver, which is also consistent with the fact that PPM corresponds to an orthogonal signal. By comparing Figure 6 with Figure 7, it can also be seen that the bit error performance of the optimal receiver of the noncoherent PPM is better than that of the suboptimal receiver at 2 dB.
## 4 Results and Discussion
### Effect of PFHE Size Parameters
#### 4.2.2 Plate Width  
First of all, it should be noted that the plate width is designed the same on both sides of PFHE. The effect of the plate width is similar to the number of fin layers. As shown in Figure 6a, when plate width varies from 0 to 0.35, pressure drop drops sharply. As shown in Figure 6b, the changing of pressure drop when the plate width is around 0.5 m has a similar reason as the number of fin layers. It seems that 2.29732 m is reasonable as the width of the plate, taking into account the limit of pressure drop of less than 10%.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\(N_{hot}\)} & \multirow{2}{*}{\(L_{w}\) (m)} & \multirow{2}{*}{\(l_{s,hot}\)} & \multirow{2}{*}{\(l_{s,cold}\)} & \multirow{2}{*}{\(s_{hot}\)} & \multirow{2}{*}{\(s_{cold}\)} & \multirow{2}{*}{\(H_{hot}\)} & \multirow{2}{*}{\(H_{cold}\)} & \multirow{2}{*}{\(t_{hot}\)} & \multirow{2}{*}{\(t_{cold}\)} & \multirow{2}{*}{\(A\)} & \multirow{2}{*}{\(\Delta p\)} & \multirow{2}{*}{Rank} \\  & & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(m2)** & **(kPa)** & \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 4: Top 20 solutions with highest normalized score.
## 5 Conclusions  
This study examined the elastic performance of clamp-based beam-to-column connections through experimental testing and finite element modeling (FEM), with a focus  
Figure 34: Normalized displacement and rotation per load for 180 mm and 260 mm frames with 2, 3, and 4 clamps.  
on reusability and sustainable structural design. Two frame geometries (180 mm and 260 mm) and various clamp configurations were evaluated under elastic conditions. The main conclusions are as follows:  
* Elastic Response: All configurations remained within the elastic range throughout both experimental and numerical analyses, confirming their applicability in dismountable structural systems;
* Clamp Quantity and Performance: Increasing the number of clamps enhanced stiffness and load capacity, with up to 27.3% gain when moving from two to three clamps, particularly in the 260 mm frame;
* Numerical Validation: FEM simulations showed over 97% correlation with experimental results, supporting their use for future design and parametric studies;
* Frame Geometry Comparison: The 260 mm frame provided superior stiffness and capacity, while the 180 mm frame offered advantages in weight, assembly, and space efficiency;
* Stress and Deformation: Higher stress concentrations were observed in configurations with fewer clamps, yet all systems remained within the elastic range, ensuring safety and performance;
* Sustainability Aspects: The removable clamp design aligns with Design for Deconstruction (DfD), promoting ease of reuse, reduced waste, and extended structural lifecycle;
* Design Guidance: For practical use, clamp-based systems should consider clamp number, preload level, and frame geometry based on loading demands and spatial constraints.  
Overall, clamp-based connections present a reliable, reusable, and sustainable alternative to conventional joints, offering structural efficiency and adaptability for modern modular and temporary applications.