esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
This study included experimental studies and analytical investigations to propose a new method to evaluate the prediction accuracy of damage locations using ANN with AE data in deep piles. The main conclusions drawn from the results are as follows:  
1. Among the six training algorithms studied in this paper, the training algorithm of \"TRAINGLM\" has the best performance for training the ANN model for predicting damage locations in deep piles.
2. The prediction accuracies of three sensor installation groups can be ranked as follows: group 1 (pile body-installation group) > group 3 (mix-installation group) > group 2 (platform-installation group). This result can lead engineers to decide that when detecting the damages of deep piles using the AE technique, the priority AE sensor installation option is pile body-installation, the second option is mix-installation (pile body and platform), and the last option is platform-installation.
3. The existence of zero values in actual values makes the MAPE infinite, and zero values can maximize the evaluation results of the SMAPE. Thus, when evaluating the accuracy of predictions using the MAPE and SMAPE, the zero values should be removed from the actual values. The result is suitable for every prediction.
4. The sensitivity of the seven accuracy metrics can be ranked as follows: MSE > SMAPE = MAPE > MAE > RMSE > R2 > R. The more sensitive the metric is, the more suitable it is for comparing the accuracy of different predictions. The result is suitable for every prediction.  
In further study, the study of the training algorithms will be extended further than the six algorithms studied in this paper. The study will be extended from a single pile to group pile.  
Conceptualization, A.J., S.W. and T.-M.O.; data curation, A.J.; formal analysis, A.J. and P.W.; Funding acquisition, S.W. and P.W.; software, A.J. and T.-M.O.; writing--original draft preparation, A.J.; writing--review and editing, S.W. and T.-M.O.; project administration, S.W. All authors have read and agreed to the published version of the manuscript.  
This work was conducted with supports from the National Natural Science Foundation of China (Grant Nos. U1602232 and 51474050), Key science and technology projects of Liaoning Province, China (2019 JH2-10100035), the Fundamental Research Funds for the Central Universities (N180701005).  
Not applicable.  
Data Availability Statement: Data sharing is not applicable to this article.  
**Acknowledgments:** This work was supported by the Brain Korea 21 FOUR Project in the Education & Research Center for Infrastructure of Smart Ocean City (i-SOC Center).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Phase Alignment of an Array Optical Telescope System Using Balanced Detection
## 2 Theories and Design
### Theories of the Structural Innovation
In this case, the output signals of the cross couple are written as Equation (4). \[ \begin{pmatrix}E_{con}\\ E_{des}\end{pmatrix} = \begin{pmatrix}\frac{\sqrt{2}}{2}&j\frac{\sqrt{2}}{2}\\ j\frac{\sqrt{2}}{2}&\frac{\sqrt{2}}{2}\end{pmatrix}\begin{pmatrix}E_{211n}\\ E_{221n}\end{pmatrix} = \begin{pmatrix}0\\ j\sqrt{2}E_{0}\end{pmatrix}  \tag{4}\]  
The amplitudes of the constructive interference and the destructive interference are given in Equations (5) and (6), respectively. \[E_{con}=A\Big{(}e^{i\varphi_{n-1}}+e^{i\varphi_{n}}\Big{)} \tag{5}\]  
Figure 3: External beam splitting structure diagram of DPSK demodulation and phase detection. \[E_{des}=A\left(e^{i\varphi_{n-1}}-e^{i\varphi_{n}}\right) \tag{6}\]  
According to the mathematical analysis formula, the function of the external beam splitting structure of DPSK demodulation and phase detection is the same as the MZI, while the input optical signal at the receiving end is uniformly generated by the same light source. The voltage output of the balance detector can be expressed as Equations (5) and (6). The first photodetector output is shown in Equation (7). \[u_{1}=i_{1}R=RBE_{con}{}^{2}=A^{2}RB\left(e^{i\varphi_{n-1}}+e^{i\varphi_{n}} \right)^{2} \tag{7}\]  
The second photodetector output is shown in Equation (8). \[u_{2}=i_{2}R=RBE_{des}{}^{2}=A^{2}RB\left(e^{i\varphi_{n-1}}-e^{i\varphi_{n}} \right)^{2} \tag{8}\]  
where \(A\) is the amplitude; \(R\) is the resistance value; \(B\) is equal to \(\eta e/h\
u\), which represents for the responsivity of the photodiode; \(\eta\) is the quantum efficiency of the photodiode; \(e\) is the amount of electron charge; \(h\) is the Planck constant; and \(\
u\) is the frequency of light. Ideally, the value of \(B\) is 1 A/W. As shown in Figure 3, the balanced detector is composed of two photodiodes with the same parameters. They work in the reverse voltage region, which converts electrical signals into optical signals and outputs them differentially. This output structure can well suppress common-mode noise and is conducive to coherent optical communication. The resulting differential voltage is described as Equation (9). \[u=A^{2}RB\left(e^{i\varphi_{n-1}+\varphi_{miss}}+e^{i\varphi_{n}}\right)^{2}-A ^{2}RB\left(e^{i\varphi_{n-1}+\varphi_{miss}}-e^{i\varphi_{n}}\right)^{2}=2A^{ 2}RB\cos(\varphi_{miss}+\Delta\varphi) \tag{9}\]  
where \(\Delta\varphi=\varphi_{n-1}-\varphi_{n}\) represents the phase difference of adjacent symbols when there is no disturbance, and the value can only be 0 or \(\pi_{c}\) and \(\varphi_{miss}\) is the phase difference of two optical signals when considering disturbance. Equation (9) includes the relative phase required by DPSK demodulation and the phase error of the sub-aperture. A detailed derivation process will be demonstrated in the third section by utilizing the phase difference through electrical signal.
First, we consider the imperfect information of cyclic frequency associated with the CSIN estimator and propose an estimation algorithm to estimate the cyclic frequency offset. The estimation algorithm is based on the phase variation of the tap weights of the adaptive FRESH filter over a certain observation time. In addition, we have derived the maximum observation period to avoid the phase ambiguity issue. Second, by exploiting the difference of cyclic frequency between the CSIN and the received OFDM signal, we have proposed using another adaptive FRESH filter as the signal restorer. Moreover, a practical procedure to train these two adaptive FRESH filter has been provided in this paper. Third, we have referred to the IEEE 1901.2 standard to built the simulation programs, include the channel model and CSIN model, to validate the effectiveness of the proposed adaptive FRESH-based receiver. Simulation results have confirmed that our proposed cyclic frequency offset estimation algorithm could suppress the cyclic frequency relative error by at least 15.45 dB. With the compensation of the cyclic frequency offset, the required values of the \(E_{b}/N_{0}\) at bit error rate (BER) of \(10^{-4}\) are 22.6 dB, 23.3 dB, and 25.2 dB for the ideal, compensated, and no CSIN mitigation cases, respectively, for the differential binary phase shift keying (DBPSK) modulation scheme. The remainder of this paper is organized as follows. Section 2 describes the system models considered in this study, including powerline channel and noise models. Section 3 expatiates on the proposed adaptive FRESH-filter receivers. Section 4 presents our simulation results. Conclusion and discussion are drawn in Section 5.
# Strongly Focused Circularly Polarized Optical Vortices Regulated by a Fractal Conical Lens
## 2 Theory Model
 \tag{6}\]  
where \(E_{+,i}(r,\varphi,z)\) and \(E_{-,i}(r,\varphi,z)\) (\(i=x,y,z\)) represent the right-hand circularly (RHC) polarized and left-hand circularly (LHC) polarized electric field, respectively. \(r,\varphi\) and \(z\) denote the cylindrical coordinates of an observation point. \(k=2\pi/\lambda\) stands for the wave vector, \(f\) is the high NA objective lens' focal length, \(\phi\) is the azimuthal angle of incident beam, and \(\theta\) is the convergence angle that varies from 0 to the maximum convergence angle \(\alpha\). \(J_{m}(\cdot)\) is a first kind Bessel function of order \(m\). For simplicity, the incident light wave's mode index is assumed to be 0, and then the transversal amplitude of the Laguerre-Gaussian beam LC\({}_{0}^{m}\) could be given by [21]  
\[E_{m}(r)=\left(\frac{\sqrt{2}r}{w_{0}}\right)^{\left|m\right|}\exp\left(-\frac {r^{2}}{w_{0}^{2}}\right). \tag{7}\]  
Obeying the sine condition, i.e., \(r=f\sin\theta\), the apodization function could be written as  
\[E_{m}(\theta)=\left(\frac{\sqrt{2}f\sin\theta}{w_{0}}\right)^{\left|m\right|} \exp\left(-\frac{f^{2}\sin^{2}\theta}{w_{0}^{2}}\right). \tag{8}\]  
Equations (2), (4)-(6), and (8) provide a general description of a circularly polarized optical vortex through a high NA objective lens system with a FCL. In the following discussion, these equations are used to investigate the distributions of intensity, phase, and intensity evolution of strongly focused circularly polarized optical vortex in cases with and without a FCL. In addition, the spin-to-orbital angular momentum conversion for optical vortex in such a strongly focused optical system is be analyzed. In the following numerical simulations, the common parameters are chosen as: \(\lambda=0.632~{}\upmu\)m, \(f=1\) mm, and \(n_{1}=1.33\).
# Dynamic Shortest Paths Methods for the Time-Dependent TSP
## 3 A Branch-and-Price Algorithm  
Our aim in the next section is to provide a state-of-the-art mixed integer programming (MIP)-based algorithm to solve the TDTSP in full generality. We begin by formally defining time-expanded graphs and establishing both an arc-based and a path-based formulation, since it is not immediately clear which approach will work better. Both formulations consist of large numbers of variables, necessitating column generation approaches. Since feasible tours correspond to acyclic paths through the time-expanded graph, the key to solving the TDTSP efficiently is: _How can we find a shortest path through the time-expanded graph which is acyclic on the original graph?_  
Optimizing over the set of acyclic paths, which we denote by \(\mathcal{P}^{*}\), corresponds to solving the TDTSP itself. The set \(\mathcal{P}\) of all paths through the time-expanded graph is much easier to handle, leading to a very fast pricing algorithm at the cost of a substantially weaker relaxation. To balance the computational effort of the pricing step and the strength of the relaxation, we generate \(k\)-cycle-free paths, that is, paths containing no cycles of size \(\leq k\), where larger values of \(k\) increase the computational effort while improving the relaxation.  
After discussing the subject of column generation, we strengthen both formulations based on valid inequalities. To this end, we review several classes of valid inequalities for the SDTSP and other related problems, adapting the class and their respective separation algorithms to the TDTSP. Lastly, we make several improvements relating to well-known MIP techniques, such as adding a branching rule and several primal heuristics.  
**Definition 1**.: _The set of reachable points in time, \(\mathcal{T}:V\to 2^{\Theta}\) is defined as_  
\[\mathcal{T}(v):=\{\theta\in\Theta\ |\ \exists(a_{1},\ldots,a_{k}),a_{1}=(s,v_{1} ),a_{k}=(u_{k},v),\theta^{\text{arr}}(a_{1},\ldots,a_{k})=\theta\}.\]  
_The time-expanded graph \(D^{\mathcal{T}}=(V^{\mathcal{T}},A^{\mathcal{T}})\) has vertices \(V^{\mathcal{T}}\coloneqq\{v_{\theta}\ |\ v\in V,\,\theta\in\mathcal{T}(v)\}\), and arcs_  
\[A^{\mathcal{T}}\coloneqq\{(u_{\theta},v_{\theta^{\prime}})\ |\ u_{\theta},v_{ \theta^{\prime}}\in V^{\mathcal{T}},\theta^{\prime}=\theta+c_{uv}(\theta)\}.\]  
We denote an arc \((u_{\theta},v_{\theta^{\prime}})\) by \((u,v,\theta)\) and assume from now on that \(c_{uv}(\theta)>0\) for all \((u,v)\in A,\theta\in\mathcal{T}(v)\). It follows that \(D^{\mathcal{T}}\) is acyclic.  
**Example 1**.: _Figure 1 shows a directed graph with travel times for each arc and its time expansion. Any tour on \(D\) can be embedded into \(D^{\mathcal{T}}\) as an \((s_{0},s_{\theta})\)-path._
## 5 Verification of Optimum Design through Local and Global Analysis
### Verification of Optimum Design through Stability Analysis  
To verify the structural stability of the wing, buckling analysis was performed using a finite element average internal load. Due to optimization, the outer skin was applied to the plate thinner, resulting in overall or local buckling more easily. Table 4 summarizes the eigenvalues and margins from the first to the fourth mode for each model as the results of the buckling analysis of the skin applying the optimal design. It can be seen that the initial buckling value of the initial model is 1.016 eigenvalue, and the minimum margin is 0.02. As the optimization proceeded, in the case of a model with only thickness optimization, the eigenvalue was 1.022, the minimum margin was 0.02, and it was confirmed that it did not converge to the minimum margin of 0.05, which is the constraint. This is because it can be seen that the minimum margin is 0.05, which converges to the minimum margin of 0.05, which is the buckling design requirement. Figure 18 visualizes and compares the primary mode results in the envelopes of each shape model. Figure 18 compares the buckling results  
Figure 17: Comparison of stress \(\sigma_{\mathrm{xy}}\) results (left: tension, right: compression).  
of the outer shell in primary mode with the initial and optimization models. Figure 18a shows the shape of buckling by twisting outward from the area under compression load due to the thick outer skin, and Figure 18b shows the shape of buckling in the entire outer skin, not in some areas.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **(a)** & **(b)** & **(c)** \\ \cline{2-4}
**Mode Level** & **Eigenvalue (\(\lambda\))** & **Eigenvalue (\(\lambda\))** & **Eigenvalue (\(\lambda\))** \\  & **/M.S(\(\lambda\)-1)** & **/M.S(\(\lambda\)-1)** & **/M.S(\(\lambda\)-1)** \\ \hline Mode I & 1.016/0.016 & 1.022/0.022 & 1.054/0.054 \\ Mode II & 1.016/0.016 & 1.022/0.022 & 1.095/0.095 \\ Mode III & 1.419/0.419 & 1.050/0.050 & 1.095/0.095 \\ Mode IV & 1.419/0.419 & 1.056/0.056 & 1.116/0.116 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison results of each model’s stability analysis.  
Figure 18: Comparison of Buckling results, 1 st Mode.
# Fed-Hetero: A Self-Evaluating Federated Learning Framework for Data Heterogeneity
## 3 Notations and Preliminaries
### Metrics to Measure Data Heterogeneity  
#### 3.3.1 Weight Divergence  
Weight divergence between clients is a technique used in FL to assess and monitor variations in model updates sent to the server by different clients. This approach providesthe server with insights into the consistency of model updates, acting as an indicator of model performance, convergence, or accuracy. When the divergence is minimal, it implies that the model is performing uniformly across the various clients.  
The weight divergence for the client \(i\) can be defined as follows:  
\[WD_{i}=\|W_{i}(t+1)-W_{\text{global}}(t+1)\|_{2} \tag{4}\]  
where:  
* \(WD_{i}\): Weight divergence for client \(i\).
* \(W_{i}(t+1)\): Model weights from client \(i\).
* \(W_{\text{global}}(t+1)\): Aggregated global model weights.
* \(\|\cdot\|_{2}\): L2 norm (Euclidean distance) of the difference between two weight vectors.
# Recent Advances in Plasmonic Sensor-Based Fiber Optic Probes for Biological Applications
###### Abstract  
The survey focuses on the most significant contributions in the field of fiber optic plasmonic sensors (FOPS) in recent years. FOPSs are plasmonic sensor-based fiber optic probes that use an optical field to measure the biological agents. Owing to their high sensitivity, high resolution, and low cost, FOPS turn out to be potential alternatives to conventional biological fiber optic sensors. FOPS use optical transduction mechanisms to enhance sensitivity and resolution. The optical transduction mechanisms of FOPS with different geometrical structures and the photonic properties of the geometries are discussed in detail. The studies of optical properties with a combination of suitable materials for testing the biosamples allow for diagnosing diseases in the medical field.  
plasmonics; photonics; fiber optic probes; surface plasmon resonance; metal; dielectric; refractive index; analyte; biosamples +
Footnote †: journal: Applied sciences
# Carbon Nanotubes-Based Hydrogels for Bacterial Eradiation and Wound-Healing Applications
## 2 Conductive Properties of CNTs
### Single-Walled and Multi-Walled Carbon Nanotubes  
Based on the number of inner layers, CNTs are categorized into single-walled CNTs (SWCNTs) and multi-walled CNTs (MWCNTs) [50; 51]. CNTs properties are varied with the number of inner layers. Different types of CNTs are presented in Figure 2. SWCNTs are composed of a single layer of a graphene sheet and have 1-2 nm diameters. The controlled catalytic conditions are required to obtain the high quality and purity of SWCNTs. Doubled-walled CNTs (DWCNTs) are generated from two distinct CNTs of inner and outer tubes. The diameter of the outer and inner tubes is approximately 2-4 nm and 1-3 nm, respectively. DWCNTs have similar properties to SWCNTs, including narrow diameter, electrical, and mechanical properties. MWCNTs are comprised of multilayer graphene sheets rolled with a diameter of 2-50 nm; the outer layer diameters can reach up to 100 nm, and the inner layer diameter is below 1 nm. SWCNTs are classified into structural models such as the Russian doll and Parchment model, depending on the arrangement of the graphene sheet in the concentric cylinder [52]. MWCNTs have defects in their structure, which favor the surface functionalizations, and these defects are increased with increasing the number of graphene sheets rolled up in MWCNTs. MWCNTs are considered potential materials for biomedical applications because of their excellent chemical, thermal, mechanical, electrical, and variable structural properties and functionalities. Additionally, CNTs show semiconducting and superconducting electron transport properties. The biocompatibility, antimicrobial, and antibacterial properties of CNTs are moderately attractive for their use in wound dressing and antibacterial treatment [13].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Properties** & **Units** & **SWCNT** & **MWCNT** \\ \hline Specific gravity (bulk) & g/cm\({}^{3}\) & 0.8–1.3 & 1.8–2.6 \\ Specific area & m\({}^{2}\)/g & 400–900 & 200–400 \\ Young’s modulus & Pa & \(\approx\)1000 & \(\approx\)1000 \\ Tensile strength & Pa & 3.10\({}^{10}\)–5.10\({}^{11}\) & 1.10\({}^{10}\)–15.10\({}^{10}\) \\ Thermal conductivity & W/m.K & 3000–6000 & 2000–3000 \\ Electrical conductivity & S/cm & 10\({}^{2}\)–10\({}^{6}\) & 10\({}^{3}\)–10\({}^{5}\) \\ Thermal stability & \({}^{\circ}\)C & 550–650 & 550–650 \\ temperature in air & \({}^{\circ}\)C & \multicolumn{1}{c}{} & \\ \end{tabular}
\end{table}
Table 2: Various properties of SWCNT and MWCNT [49].
###### Abstract  
Conventional cement concrete pavements often suffer from rapid skid resistance degradation and excessive traffic noise, necessitating effective solutions. This study investigates exposed aggregate concrete (EAC) through orthogonal experimental methods to evaluate the effects of four mix design parameters--water-binder ratio, sand ratio, coarse aggregate volume ratio, and proportion of aggregates > 9.5 mm--on surface texture characteristics, skid resistance and noise reduction (SRNR) performance, and mechanical properties. The optimal EAC mix proportions were developed, and the correlations between surface texture characteristics and SRNR performance were established. Results indicate that the proportion of aggregates > 9.5 mm significantly influences surface texture characteristics and SRNR performance. The optimal mix proportions (water-binder ratio: 0.43, sand ratio: 31%, coarse aggregate volume ratio: 42%, and proportion of aggregates > 9.5 mm: 50%) exhibited superior mechanical properties, achieving a 31.5% increase in pendulum value and a 6.48 dB reduction in tire/surface noise compared to grooved conventional concrete. The noise reduction frequency range is mainly concentrated in the mid-high frequency range of 1.5-4.0 kHz, which is more sensitive to the human ear. High correlations were observed between the surface texture characteristics and SRNR performance. Specifically, noise value decreased progressively with increasing exposed aggregate depth, while the pendulum value exhibited a trend of initial decrease, followed by an increase and subsequent decrease in response to the elevated exposed aggregate area ratio. Compared to traditional cement concrete pavements, the optimized EAC, while maintaining mechanical properties, exhibits superior SRNR performance, providing a valuable reference for the construction of high SRNR cement concrete pavements.  
Keywords:road engineering; exposed aggregate concrete; surface texture characteristics; skid resistance; noise reduction; mechanical properties; orthogonal experimentation
## 3 Results
### Water Migration during Cold Gas Injection  
As mentioned above, water redistribution under the coupling effect of displacement and phase transition will affect the temperature field. Water in the vertical freezing model was not only adsorbed by sand but also affected by gravity; meanwhile, the upward force was exerted by the gas flow. Therefore, the combination of these three forces determines the direction of water migration. Figure 7a,b depicts the redistribution of water after experiments with initial water content of 0.2 and 0.4, respectively. From Figure 7a, it can be seen that except for the significant reduction at the inlet, the other parts decreased slightly--roughly in the range of 0.16-0.2. Obviously, the gas humidity was small at the entrance; a part of the water in the pores would be carried away by the gas flow (drying effect), which reduced the water content. Due to the low initial water content, most of the water was encapsulated on the sand surface and adsorbed, even under the displacement of gas flow and gravity, and it was difficult to migrate to a greater extent: it only evaporated under the drying effect of the gas flow. Increasing the flow rate of cold gas would also increase the volatilization of water. Therefore, when the flow rate was 25 L/min, the overall moisture content was relatively low after the experiment. From Figure 7b, it showed that water migration at high initial water contents presented the opposite law. The moisture content near the inlet was greatly increased, reaching between 0.6 and 0.75, and the further it was from the inlet, the lower the saturation of water. As only a part of the water was adsorbed on the surface of the sand particles, which was difficult to move, the rest could migrate under the action of gravity and gas flooding. According to the foregoing analysis, after stable gas flow channels formed, the gas flow rate outside the channels was low, which made the water migrate downward under the dominated action of gravity. It was noteworthy that when the initial water content was 0.4, increasing the flow rate would increase the upward force of water migration, thus making the water content at the lower end of the freezing model less than 15 L/min, while the upper end of the model was the opposite. However, freezing of the water started from the bottom; the frozen water in the pores also attracted water in the upper part to move toward the frozen front. Therefore, the attraction of the frozen front to water would also redistribute water in the pores.  
Figure 7: Water distribution after cold gas injection (**a**) Initial water content was 0.2, (**b**) Initial water content was 0.4.