esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Experiment and Discussion
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**UAV\({}^{*}\)**} & **Statistical Analysis** & \multirow{2}{*}{**CC-MPSO**} & \multirow{2}{*}{**PSO**} & \multirow{2}{*}{**MPSO**} & \multirow{2}{*}{**ECPO-ME**} & \multirow{2}{*}{**ACO**} & \multirow{2}{*}{**RI-MAC**} \\ \cline{5-5}  & BEST & **0.24595** & 0.20165 & 0.21151 & 0.21254 & 0.19456 & 0.23458 \\  & WORST & 0.20235 & 0.15498 & 0.15687 & 0.18456 & 0.14354 & **0.21045** \\  & MEAN & **0.22546** & 0.17416 & 0.17945 & 0.19453 & 0.15235 & 0.22054 \\  & MEDIAN & **0.23015** & 0.16941 & 0.17456 & 0.19154 & 0.16020 & 0.21945 \\  & SD & 0.00921 & 0.02481 & 0.02358 & 0.00985 & 0.02157 & **0.00845** \\ \hline \hline \multirow{5}{*}{UAV2} & BEST & **0.28209** & 0.25138 & 0.26165 & 0.26786 & 0.24198 & 0.27954 \\  & WORST & 0.26135 & 0.19465 & 0.21546 & 0.20589 & 0.18454 & **0.26735** \\ \cline{1-1}  & MEAN & 0.27135 & 0.22054 & 0.23024 & 0.23498 & 0.21548 & **0.27218** \\ \cline{1-1}  & MEDIAN & **0.27984** & 0.22157 & 0.22098 & 0.23759 & 0.22085 & 0.26549 \\ \cline{1-1}  & SD & 0.00584 & 0.02565 & 0.01546 & 0.00984 & 0.01489 & **0.00448** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Statistical analysis of the outcome of all algorithms for scenario 4. Figure 7 shows the optimal search paths for each UAV for all tested algorithms. In the figure, the purple path indicates the path generated by UAV1, the white path indicates the path generated by UAV2, and the red dot indicates the initial position of UAVs. It is worth mentioning that the presented target probability map depicts the target's final step. By comparing the present map with the one shown in Figure 7, it is feasible to examine the evolution of targets and track their progression. The rest of the scenarios are represented in the same way as this figure. It can be seen that in the path planned by CC-MPSO, UAV1 can search along the direction of the target's motion trajectory, and UAV2 can quickly approach the high-probability target area at an initial position farther away from the target. (2) Scenario 2  
The results for scenario 2 are tabulated in Table 3. The acquired results indicate the following conclusions:  
* For the first UAV, the CC-MPSO algorithm achieves the best results among the three statistical parameters \"BEST\", \"MEAN\", and \"SD\", and \"RI-MAC\" achieves the best results among the two statistical parameters \"WORST\" and \"MEDIAN\". * For the second UAV, the CC-MPSO algorithm achieves optimal results in all statistical terms. Figure 8 shows the optimal search paths for each UAV for all tested algorithms. In the figure, the purple path indicates the path generated by UAV1, and the white path indicates the path generated by UAV2. The path generated by the CC-MPSO algorithm closely approximates the target's motion trajectory. Both UAVs successfully locate the nearest high-probability grid to their initial positions at the end of the path, demonstrating the strong predictive capability of the CC-MPSO algorithm.
# Review of Flexible Piezoresistive Strain Sensors in Civil Structural Health Monitoring
## 4 Optimization of Sensor Performance
### Improvement of Stretchability  
As for civil SHM, the stretchability of traditional strain sensors that is below the 2% range is sufficient for regular deformation monitoring but insufficient for detection of structural cracks and damages [29]. Hence, a wider working range is necessary for large-strain and damage tracking. Basically, stretchability of the flexible sensor depends on not only the elasticity of the polymer matrix, but also the stability of the conductive network during stretching [78].  
There are two categories of approach to improve the stretchability. The first category is adopting stretchable conductive materials, as well as elastic substrates. Commonly used stretchable conductive fillers such as 1D CNTs, 1D AgNPs and 2D graphene have high-aspect ratios that can form many twists in the network [102; 103]. These nanomaterials have been either mixed into an elastomeric polymer matrix or integrated in the middle layer/on the top layer of substrates [104; 105]. In addition, a strong binding between the conductive materials and the polymer substrates is valuable for achieving high stretchability [80]. In a word, approaches of the first category are based on exploiting the intrinsic properties of sensor materials; therefore, they are helpful for simple fabrication and cost-effective production [106]. The second category is the special structural design of the flexible sensor, including out-of-plane designs such as 3D wavy, wrinkled and coiled shapes [107; 108], as well as in-plane designs such as buckled, serpentine pattern, and mesh-like geometries [109; 110]. Note that these designs help sensors to withstand much larger strain and there is no need to be concerned about sensors breaking during stretching [111]. Nevertheless, fabrications of these sensors require more complicated multi-processes and higher costs than the approaches of the first category [112].  
Furthermore, there exists a common problem in pursuing high sensitivity and high stretchability at the same time [113], which is the balance between these two parameters. Specifically, a wide sensing range requires that the active material can keep its structural connections at a high strain level, whereas detection of subtle strain demands the resistivity of the conductive fillers to change significantly under a low strain level [91]. In this case, proper design and control of the connection types of sensing materials are vital to guarantee both high sensitivity and high stretchability [114]. For example, [PERSON] et al. developed an ultrasensitive and highly stretchable strain sensor based on thin graphite films with parallel microcracks coated on top of Ecoflex, showing a high GF value of 5.2 \(\times\) 10\({}^{2}\) at a large strain of 50% [50]. Furthermore, a combination of microcracks and scale-like structures was utilized to develop a high-performance strain sensor that possesses high sensitivity, high stretchability (GF value of 10\({}^{7}\) at 120% strain), fast response and remarkable stability.
## 1 Introduction
[19] developed an optimization design method for the cable forces and cross-sectional areas of a hybrid cable-stayed bridge during its construction phase, based on finite element numerical models and iterative computational methods. In addition, numerous advanced intelligent optimization algorithms have been increasingly applied to the optimization of cable forces in cable-stayed bridges to achieve reasonable construction states [20; 21; 22]. In addition to optimizing cable forces in cable-stayed bridges, the assessment of cable forces control during both the construction and lifespan phases of bridges has also been a subject of extensive research. [PERSON] et al. [23] and [PERSON] et al. [24] conducted studies on the condition assessment of cable-stayed bridges, leading to several valuable conclusions. Additionally, the stability performance of stay cables has been another key area of research. [PERSON] et al. [25,26] and [PERSON] et al. [27] conducted extensive studies on the stability of stay cables, focusing on their dynamic characteristics and the parameters influencing stability. Their numerical investigations yielded several valuable conclusions. However, existing traditional methods for optimizing the reasonable construction state of cable-stayed bridges still have certain limitations: Due to the effects of structural system transformations, concrete shrinkage and creep, and geometric nonlinearities, the calculation results from the inverted disassembly method and the forward analysis method often exhibit great difference. Although the forward-inverted disassembly iterative method can account for these factors, including structural system transformations, concrete shrinkage and creep, and geometric nonlinearities, it requires complex iterative calculations using both the forward-inverted disassembly iterative methods. Therefore, there is a need to explore more appropriate optimization objectives and more efficient optimization techniques that align with the structural characteristics and construction methods of large-span cable-stayed bridges with dense cable-stayed steel box girders, in order to determine the reasonable construction state for this type of bridge. A novel optimization method for determining the reasonable construction state of cable-stayed bridges is proposed in this paper. Firstly, the principle of determining the reasonable construction state of steel box girder cable-stayed bridge is clarified. The problem of determining the reasonable construction state of steel box girder cable-stayed bridge is then formulated as a multi-objective optimization model and solved sequentially using the weighted sum method within the evaluation function framework, followed by the damped Newton method to address unconstrained single-objective optimization problems. To validate the effectiveness of this method, it is applied to optimize the construction state of a large-span and double-tower steel box girder cable-stayed bridge, integrating it with the orthotropic iterative finite element method.
## 7 Future Directions
### AI-Driven Network Management  
AI and ML techniques can significantly enhance the administration of hybrid THz-FSO networks. It may be concluded that climate-sensitive models derived from the collected data might allow for the real-time reorganization of the frequency range and reallocation of resources, thereby improving the network availability and rate. AI optimum control methods also reduce latency effects in time-critical applications like semi-/fully autonomous cars and telemedicine.
## 2 Materials and Methods
### Biomarkers of Oxidative Stress and Antioxidant Capacity  
Thiobarbituric acid-reactive substances (TBARS), the serum concentration of oxidized low-density lipoprotein (OxLDL), 8-iso prostaglandin F2\(\alpha\) (8-iso-PGF2a), and advanced oxidation protein products (AOPPs) were measured as oxidative stress biomarkers. The TBARS method measures serum malondialdehydedehyde (MDA), which is formed during lipid peroxidation of polyunsaturated fatty acids. MDA reacts with thiobarbituric acid (TBA), forming a pink chromogen (TBARS). A Nanophotometer (P300 UV/VIS, IMPLEN) at 572 and 532 nm was used to measure the absorbance of the sample, with MDA used as a standard (\(\upmu\)M MDA) [24]. Serum concentrations of OxLDL (MyBioSource, MyBioSource Inc., San Diego, CA, USA), 8-iso-PGF2a (MyBioSource, MyBioSource Inc., San Diego, CA, USA), and AOPPs (MyBioSource, MyBioSource Inc., San Diego, CA, USA) were measured by commercially available enzyme-linked immunosorbent assay (ELISA) kits on a compact absorbance reader for 96-well microplates (BioRad PR 3100 TSC, Bio-Rad Laboratories, CA, USA) at 450 nm.  
As biomarkers of antioxidant defense, the ferric-reducing ability of plasma (FRAP) and serum antioxidant enzyme activity were measured. The FRAP method uses antioxidants as reductants, wherein ferric ions (Fe\({}^{3+}\)) at low pH are reduced to ferrous ions (Fe\({}^{2+}\)), and this reaction causes a blue-colored ferrous-tripyridtriazine complex to form. A Nanometer (P300 UV/VIS, IMPLEN) at 593 nm with Trolox as a standard (mM/L Trolox) was used to measure the absorbance of the sample [25]. The enzyme activities of superoxide dismutase (SOD), glutathione peroxidase (GPx), and catalase (CAT) in serum samples were determined using a Lambda 25 UV-Vis spectrometer equipped with the UV WinLab 6.0 software package (PerkinElmer For the Better, Waltham, MA, USA) according to well-established protocols [26; 27; 28]. The measured results are expressed as U (mg protein\({}^{-1}\)). The concentration of proteins in the samples (mg mL\({}^{-1}\)) was determined using Bradford reagent at 595 nm (Bradford Reagent B6916, Sigma Aldrich), according to the manufacturer's protocol, using bovine serum albumin as a standard. Serum antioxidant enzyme activity assays were performed at the Biochemistry Laboratory at the Department of Biology, University of Osijek.
## 3 Method
### Interaction with Voice Agent  
Three types of voice agents were developed based on whether they used a machine or human voice and on the application of the humanized speech strategies (Table 3). All three types of agents were in-vehicle agents that supported voice-based conversations in Korean and used a formal, polite, sentence-ending style. Agent A used a machine voice without humanized speech strategies. Agent B employed a human voice but without humanized speech strategies. Agent C utilized a human voice combined with humanized speech strategies. This progression from Agent A to Agent C was designed to increase the level of anthropomorphism in the interactions. The human voice used for Agents B and C was female. Humanized speech strategies are designed to make interactions more natural and engaging by incorporating elements that mimic human conversation. Based on previous research, these strategies include empathetic responses, explanations of actions, agreement, providing advice, and asking questions [45; 46; 47; 48]. Agent C incorporated strategies such as emotional responses, reasons for decision, transparency, agreement, and asking questions, each contributing to a more human-like interaction. Emotional responses allow the agent to express empathy, making the interaction more engaging. Reasons for decision provide users with explanations for the agent's actions, fostering trust and understanding. Transparency involves the agent openly communicating its decision-making processes and actions, which helps to build trust by ensuring that the user understands what the agent is doing and why, thereby reducing uncertainty and fostering confidence in the interaction. Agreement helps to align with the user's preferences, creating a sense of cooperation. Lastly, asking questions encourages a two-way dialogue, making the interaction more dynamic and personalized. To stimulate interactions with the voice agents in the driving scenarios, text-to-speech (TTS) software was used. Conversations between the driver and the voice agents were recorded based on pre-established driving scenario scripts, and the voice agents were generated using a female TTS voice provided by Selvy Voice.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Participant Characteristics** & **Males** & **Females** \\ \hline Number of participants (\(n\)) & 13 & 17 \\ \hline Average age (_years_) & 24.23 & 22.24 \\ \hline Age range (_years_) & 22–28 & 20–25 \\ \hline Driving frequency (_more than 2 times per week_) & 9 & 6 \\ \hline Experience with voice agents (\(n\)) & 10 & 15 \\ \hline Possession of driving license (\(n\)) & 11 & 11 \\ \hline \hline \end{tabular} Note: (\(n\)) indicates frequency.  
\end{table}
Table 2: Demographic characteristics of participants by gender.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Human-Likeness Characteristics** & **Agent A** & **Agent B** & **Agent C** \\ \hline Level of anthropomorphism & Low & Middle & High \\ \hline Voice & Machine & Human & Human \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of human-likeness characteristics of the three agents.
## 3 Performance Analysis of iPyroxy Block
### S21 Transmission Coefficient With and Without iPyroxy Block  
The transmitted signal is measured when an electromagnetic wave interacts with the material in the iPyrovy block to analyze the S21 parameter. Considerably more signal attenuation is suggested by a low S21 value than a high one, indicating inefficient transmission and little signal loss.
# Mode Shape-Based Damage Detection Method (MSDI): Experimental Validation
## 1 Introduction
During this research, finite element analysis (FEA) was performed to obtain the mode shapes of plate models with different damage cases and boundary conditions. It was concluded that the MSDI method can be used to accurately locate single and multiple damages on plate-like structures and that it has the capability to distinguish damages with different levels of severity. By comparing the MSDI method to the \(l_{1}\)-norm regularized finite element model updating method, researchers obtained similar results in damage localization [31]. The advantage of the MSDI method is that it does not require an additional tool, for example, a finite element model or algorithm-based massive measurement data such as artificial intelligence [32], machine learning [33], and deep learning [34] for damage detection. Unlike existing damage detection methods based on mode shape changes, this method is based on a minor difference between compared undamaged and damaged mode shapes. In this way, it is possible to determine the damage at the earliest stage of its occurrence, and at the same time exclude the influence of uncertainties (noise) that may significantly affect the change in mode shapes. The aim of this study was to validate the MSDI method on the basis of the experimental model of a reinforced concrete (RC) plate under 10 different damage cases. Operational modal analysis (OMA) [35] was used for the determination of dynamic properties (natural frequencies, mode shapes, and damping ratios) of the plate in the undamaged and damaged state. The MSDI values were calculated for all damage cases and graphically presented. The article is structured as follows. The governing equations of the MSDI algorithm are shown in Section 2.1. The experimental setup and procedure as well as the damage simulation are presented in Section 2.2. The results are summarized and discussed in Section 3. Section 3.1 deals with the effect of the damage level on the MSDI values at a single damage location. Section 3.2 deals with the effect of the damage zone size, and Section 3.3 deals with damage detection at multiple damage locations. Section 4 presents the conclusions.
# Analysis of Dust Detection Algorithms Based on FY-4A Satellite Data
## 3 Methods
### Infrared Difference Dust Index (DST)  
As Section 2.2.2 described, the higher the DST, the greater the possibility of dust. The thresholds for DST was set to 16 and pixels greater than 16 were considered to be dust.
# Integrated Optical Phased Arrays for Beam Forming and Steering
## 8 Applications & Potential Alternatives
### Applications
#### 8.1.5 OPAs for Special Beam Generation  
Special beam generation is potentially used for applications at the cutting edge of lightwave technologies, such as communications, sensing, optical micromanipulation, scalable laser lithography, optical coherence tomography, and even quantum information. Some special optical beams can be generated by OPAs, such as beams with focusing ability in the near field [154], quasi-Bessel beams [155], and beams carrying OAM states [47; 156].  
Compared with standard far-field diffracting beam generation, OPA-based beam generation presents a new functional modality of near-field focusing. A 512-antenna splitter-tree-based OPA produced a spot size of 7 \(\upmu\)m at a focal length of 5 mm. A spot size of 21 \(\upmu\)m at a focal length of 5 mm was achieved by a 1024-antenna pixel-based OPA. A 10,000-antenna pixel-based array enabled the same spot size at a focal length of 10 mm [154]. Integrated OPAs could generate quasi-Bessel beams in the near field. A 1D WGA-based OPA architecture was modified to experimentally demonstrate an on-chip quasi-Bessel-beam with a Bessel length of \(\sim\)14 mm and \(\sim\)30 \(\upmu\)m power FWHM [155]. A hybrid PIC composed of a SiO\({}_{2}\) planar lightwave circuit and a 3D EF OPA demonstrated 15 OAM states for free-space space-division-multiplexing coherent optical transmission [47]. A Si WGA-based OPA generated light-carrying optical OAM with states of \(l\) = \(\pm\)1 and \(l\) = \(\pm\)3 [156].
# Design and Control of an Ultra-Low-Cost Logistic Delivery Fixed-Wing UAV
## 2 Overall UAV Design
### Wing Design
#### 2.5.2 Fuselage Design  
The aerodynamically designed fuselage, with an upward-tilting nose to protect the camera, uses dual motors for CG balancing and features a slender, reinforced structure with bulkheads and carbon tubes for enhanced torsional resistance and bending moment management, as detailed in Table 5 and illustrated in Figure 16's complete aircraft aerodynamic analysis using Fluent.  
Because of the adoption of a twin-engine, single-wing H-tail configuration, the stability is favorable, and the turning radius is reduced to a reasonable range because of the advantage of the differential thrust from the twin engines. Utilizing a high-aspect-ratio trapezoidal wing, the wing loading reaches approximately 65 g/dm\({}^{2}\) under full mission payload conditions. Employing the NACA 4412 symmetrical airfoil necessitates a certain manual launch acceleration at takeoff. Regarding the payload capacity, tests have shown that even when equipped with a GoPro 6 and an additional 100 g of weight, it can still takeoff with the mission payload, although the climb rate is low, prompting an increase in the motor's down-angle by 1\({}^{\circ}\) in later stages. Simulating logistic delivery missions, the UAV uses a 500 mL water bottle to mimic small-volume items. Under light wind conditions, through multiple tests, the accuracy of the delivery to the target area is within a 3 m range.  
Figure 14: Double vertical tail design of the drone in this article.  
Figure 15: Design diagram of horizontal and vertical tail fins.