esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Photoluminescence Spectra  
Photoluminescence spectra were collected separately from the emitting QD and the grating output couplers (vertically above the QD and output coupler). As shown in Figure 3, the exciton peak (neutral exciton X) of the QD was at 1.5722 eV, which was excited by a 532 nm continuous wave laser with a power of 4.7 \(\upmu\)W. The spectra exhibited exactly the same features, not only in terms of the spectra position, but also the width of the sharp lines, which confirms that the emission from the grating output coupler comes from the emitting quantum dot. The full width at half-maximum for the emission peak collected both from the emitting QD and the grating output coupler were almost the same with a value of 180 \(\upmu\)eV. The photoluminescence intensity collected at the point of the output coupler, which was estimated to be larger by the finite difference time domain (FDTD) simulated results, was actually less than that collected at the point of QD. As above-mentioned, the coupling efficiency from the single emitter to the waveguides was 38% for each direction and the out coupling efficiency of the grating couplers to scatter emission out of the on-chip plane into detection apparatus was about 40%. Then, about 15.2% of the photoluminescence from the QD was collected from the point of the grating coupler. The simulated collection efficiency directly from the QD was 9.1%, but from the experimental result, the photoluminescence intensity collected at the output coupler was about 2/3 of the photoluminescence intensity collected at the QD. A reason for this could be the out-coupler's emission efficiency and the waveguide transmission efficiency was not as large as the simulation result as the real structure was not as perfectly as designed when fabricated. In addition, permittivity of the real AlGaAs material used for our sample may be slightly different from the value used for simulation from the literature [29], while a sight deviation of the imaginary part of the refractive index of AlGaAs will increase the waveguide loss drastically. Therefore, an optimized design needs to be carried out to improve the transmission and out coupling efficiency in order to obtain more photon counts.  
Figure 2: Schematic of the optical detection setup.
# Artificial Intelligence and Employment: A Delicate Balance Between Progress and Quality in China
## 3 Material and Method
### Model Specification
#### 3.4.2 Diagnostic Tests and Theoretical Implications  
The diagnostic tests validate the robustness of our two-way fixed effects model, ensuring its suitability for analyzing AI's heterogeneous regional impacts. Three key findings emerge:  
1. Breusch-Pagan Test (\(\chi^{2}=1.24\), \(p=0.27\)): The absence of significant heteroscedasticity confirms that residual variances are consistent across regions. This reinforces the reliability of our estimates for H2 (AI's disproportionate impact on low-skilled workers), as unobserved regional factors (e.g., informal economies) do not distort the results. This aligns with studies in Brazil and India, where heteroscedasticity obscured automation's effects.
2. Wooldridge Test (F = 0.89, \(p=0.35\)): No autocorrelation indicates that AI's employment impacts are not confounded by time-dependent omitted variables. This rigor addresses a gap in prior work and strengthens causal claims for RQ1 (mechanisms driving AI's effects).
3. Shapiro-Wilk Test (W = 0.98, \(p=0.12\)): Approximately normal residuals validate parametric inference, ensuring that the modest negative coefficient for AI adoption (\(-0.0000355^{***}\)) in Table 3 reflects true effects rather than skewed errors. This sup ports H3, as policy interventions in eastern China (e.g., reskilling subsidies) mitigate displacement risks.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Statistic** & **Baseline Model** & **Robust Model** \\ \hline AI Adoption (Coefficient) & \(-0.0000355\)*** & \(-0.0000348\)*** \\ \hline  & (0.000008) & (0.000007) \\ \hline Breusch-Pagan Test (\(p\)-value) & 0.27 & - \\ \hline Wooldridge Test (\(p\)-value) & 0.35 & - \\ \hline Shapiro-Wilk (\(p\)-value) & 0.12 & - \\ \hline Observations & 1.755 & 1.755 \\ \hline R\({}^{2}\) & 0.9297 & 0.9295 \\ \hline \end{tabular} Notes: Robust standard errors in parentheses. *** denotes 1% significance. Diagnostic tests confirm no severe violations of model assumptions.  
\end{table}
Table 3: Baseline regression.  
Figure 4: Schematic diagram of research methodology.  
By rigorously testing assumptions, we advance methodological standards in AI -employment research, which often neglects diagnostics. Our findings highlight that AI's labor market impacts are neither uniform nor inevitable but rather mediated by policy and infrastructure--a critical insight for developing economies.
## 3 Beamforming of the Miniaturized L-Array
### Adaptive Beamforming  
In order to make the array get better beamforming effect according to the actual signal environment, many adaptive digital beamforming (ADBF) algorithms have been developed and widely used [22; 23; 24]. By adaptively adjusting the weight vector according to the signal environment, ADBF can perform better spatial filtering to enhance the desired signal and suppress interference. Among them, the sampling matrix inversion (SMI) beamformer [25] and the Eigenspace-based beamforming (ESB) algorithm are two typical adaptive beamforming methods [26]. However, the application effect on the miniaturized L-array is rarely reported.
## 5 Controller Optimization Procedure
(15.6126 s), WOA (15.2957 s), and ALO (15.0948 s) presented the largest settling times. Notably, it was found that the overshoot was not common among all algorithms, and the worst behavior was demonstrated by the GWO (\(-\)51.074 mHz) and ALO (\(-\)50.361 mHz). The convergence curve analysis revealed that the HHO, WOA, and ALO demonstrated early convergence, whereas the performance of the GWO and JSO decreased until the end of the iterations. All algorithms successfully found gain values that led the system to stability for all types of controllers. The convergence graphs indicated that the ALO algorithm presented the fastest convergence, followed by the HHO, GWO, and WOA. Further analysis showed that the HHO and WOA produced the lowest ITAE values for the PIDF controller, whereas the ALO and GOA showed the best results for the FOPID controller, and the GWO and JSO showed the lowest ITAE values for 2 DOF-PIDF. Figure 8 shows the frequency response for the best triad of the PIDF, FOPID, and 2 DOF-PIDF controllers, which were tuned with the JSO algorithm. It was observed that the JSO consistently produced the smallest objective function values across all three controllers, with no overshoot. Additionally, the frequency response of the 2 DOF-PIDF-JSO controller showed a shorter settling time (improved by 74.67% and 77.95% for the PIDF-JSO and FOPID-JSO controllers, respectively) and less overshoot (improved by 16.56% and 17.13% for the PIDF-JSO and FOPID-JSO controllers, respectively), as depicted in Figure 8. On average, the 2 DOF-PIDF controller yielded the lowest objective function, followed by FOPID (deterioration of 85.93% compared to the JSO) and PIDF (deterioration of 87.64% compared to the JSO). These findings suggest that the JSO algorithm can be a useful tool for optimizing gain values for various types of controllers, with the potential to improve system performance and stability. At this point, it should be noted that deciding the superiority of one algorithm over another is challenging, as the no-free-lunch (NFL) theorem explains. The NFL theorem states that there is no single algorithm that can correctly solve every problem. Rather, many problems are best suited for different algorithms. In this particular case, the JSO, which is superior in solving the shipboard microgrid LFC problem, may have difficulty in solving another type of problem.
# Leather Defect Detection Based on Improved YOLOv8 Model
## 1 Introduction
(2019) conducted extensive experiments on leather defect detection using state-of-the-art YOLO (You Only Look Once) series real-time inspection models. The results demonstrate that the YOLO series adequately meets the speed and accuracy requirements for real-time inspection in industrial environments. Among them, the YOLOv5 ([PERSON] et al., 2019) model is lightweight and eco-friendly, making it easy to deploy and train on devices. YOLOv6 ([PERSON] et al., 2019) is an improved model tailored for industrial detection, resulting in a high recall rate. YOLOv7 ([PERSON] et al., 2019) performs excellently in single target detection. Finally, YOLOv8 ([PERSON] et al., 2019) achieves the best performance with the highest detection accuracy and comparable detection speeds, demonstrating the strongest overall performance in detecting multiple defects. In conclusion, the current research on leather defect detection has made considerable progress. However, leather defect data exhibit a variety of spatial scales and aspect ratios: defects occur in different spatial scales, and defects of the same type vary significantly in size and shape. This poses significant challenges for defect detection. Therefore, we propose a lightweight and improved model, YOLOv8-AGE, for leather defect detection based on YOLOv8 as the baseline model. Our contributions are as follows:  
1. The EMA ([PERSON] et al., 2014) (Efficient Multi-scale Attention) mechanism has been incorporated into the backbone of the model, enabling better focus on key defect information in images and resistance to interference from leather texture. 2. Based on the multi-scale structure concept of AFPN (Asymptotic Feature Pyramid Network), AFPN ([PERSON] et al., 2014) is integrated with the VOVGSCSP module containing GSConv ([PERSON] et al., 2014) (Grouped Shuffle Convolution) into the model's architecture, aiming to enhance the model's multi-scale detection capability and reduce model complexity. 3. The concept of shared convolutional layers is introduced to optimize the design of the YOLOv8 detection head, thereby reducing model parameters. 4. We have created a well-labeled leather defect dataset containing common leather defect classes, which facilitates training of the leather defect detection model.
# A Bayesian Control Chart for Monitoring Process Variance
## 4 The Construction of the EWMA Variance Chart with the Bayesian Approach  
Because observed statistic \(M_{t}\) is discrete and the beta-binomial distribution is asymmetric, the in-control ARLs of the proposed Shewhart-type Bayesian variance chart cannot always satisfy the required in-control ARLs, such as 370.4. The same reason results in the anomaly that the out-of-control ARLs of the proposed Shewhart-type Bayesian variance chart do not inversely change with \(n\) as they normally should.  
The EWMA chart has been used to monitor small shifts of process variance quickly and effectively. Moreover, the characteristics of the moving average of observed statistics may ease the disadvantages of discreteness and asymmetry.  
We define a new EWMA statistic as:  
\[EWMA_{t}=\lambda M_{t}+(1-\lambda)EWMA_{t-1},\ 0<\lambda\leq 1, \tag{25}\]  
where \(\lambda\) is the smoothing parameter. Because \(E(M_{t})=\frac{n\pi}{\hat{\alpha}+\hat{\beta}}\) and \(M_{0}\) is set to be the mean of \(M_{t}\), the mean of \(EWMA_{t}\) is specified as:  
\[E(EWMA_{t})=\frac{n\alpha}{\hat{\alpha}+\hat{\beta}}\times\Big{(}1-(1-\lambda )^{t}\Big{)}. \tag{26}\]On the other hand, because \(Var(M_{t})=\frac{n\alpha\beta(\alpha+\beta+n)}{(\alpha+\beta)^{2}(\alpha+\beta+1)}\), the variance of \(EWMA_{t}\) is specified as:  
\[Var(EWMA_{t})=\frac{n\alpha\beta(\alpha+\beta+n)}{(\alpha+\beta)^{2}(\alpha+ \beta+1)}\ \times\frac{\lambda}{2-\lambda}\ \times\Big{(}1-(1-\lambda)^{2t}\Big{)}. \tag{27}\]  
Thus, central limit theorem would ensure that the \(EWMA_{t}\) will asymptotically follow from a continuous symmetric distribution with [15]  
\[E(EWMA_{t})=\frac{n\alpha}{\alpha+\beta} \tag{28}\]  
and  
\[Var(EWMA_{t})=\frac{n\alpha\beta(\alpha+\beta+n)}{(\alpha+\beta)^{2}(\alpha+ \beta+1)}\ \times\frac{\lambda}{2-\lambda}. \tag{29}\]
# Physics-Informed Deep Learning For Traffic State Estimation: A Survey and the Outlook
## 2 Preliminaries and Related Work
### Traffic State Estimation
#### 2.2.2 Data-Driven Approach  
The data-driven approach aims to learn traffic dynamics directly from data. Machine learning extracts knowledge, patterns, and models automatically from large volumes of data. DL, especially DNN, has revived the interest of the scientific community since 2006 [54]. Using ML for TSE is primarily focused on data imputation leveraging temporal or spatial correlations, including autoregressive integrated moving average [55], probabilistic graphical models [56], k-nearest neighbors [57], principal component analysis [58; 59], and long short-term memory models [60]. The majority of these methods assume that a traffic quantity at a time interval or within a cell depends on its historical or neighboring values, regardless of the physical characteristics of traffic flow. Accordingly, the data-driven approach is not as popular as the model-based one and does not achieve as high an accuracy as the latter [12]. More recent ML techniques aim to model nonlinearity in traffic dynamics,  
Figure 4: Fundamental diagram (red line) with data (blue dots).  
leveraging deep hidden layers together with the sparse autoregressive technique [61] and fuzzy neural networks [62]. With the advantage of both model- and data-based approaches, it is natural to consider a hybrid one for TSE.
# A 60-GHz Ultra-Thin and Flexible Metasurface for Frequency-Selective Wireless Applications
## 3 Results and Discussion
### Parametric Analysis and Optimization of the Results of the 60-GHz Metasurface
#### 3.2.1 Angle of Incidence  
To inspect the effect of the angle of the incident wave on the S-parameters results of the reflection and transmission coefficients and reflection phase of the metasurface, the numeral analysis with various angles of the incident wave is carried out for the proposed free-standing band-pass metasurface design in the CST software. Figure 5 shows the reflection phase, reflection coefficient and the transmission coefficient with the varying angles of incidence of the wave in the range of \(0^{\circ}\) to \(50^{\circ}\). From the reflection phase plot in Figure 5a, it can be deduced that the phase is dependent on the incidence  
Figure 4: Simulated plots of magnitude and phase of the 60-GHz flexible metasurface: (**a**) Reflection and transmission coefficients plots, (**b**) Phase of the reflection and transmission coefficients.  
angle. This has been observed that as the incidence angle increases, the reflection phase angle also increases. The reflection coefficient and transmission coefficients from Figure 5b,c, on the other hand, show relatively lesser dependence on the variation of the scan angle of incidence at 60 GHz. Since the direction of incidence is relative to the top and bottom of the designed dual loop metasurface, it is expected that the bandpass frequency should remain stable regardless of the direction of external EM signal without any significant alteration. The reflection coefficient in the whole angle of incidence shows it is almost complete (less than \(-0.2\) dB) while the transmission coefficients are almost blocked (\(--50\) dB). A wide bandwidth greater than 9 GHz at \(-10\) dB reference is achieved that covers the 60 GHz band of interest in all the considered angle of incidence.
## 5 Conclusions  
A low-profile omnidirectional vehicle antenna operating in the VHF band was designed, optimized, fabricated, and measured. The proposed antenna adopted a QMSIW structure for a size miniaturization and was implemented with two shorting pins at the edges for omnidirectional radiations. The antenna was optimized using the full-wave simulation tool Ansys HFSS, and then fabricated and tested in the lab. The dimension of the antenna was 125 cm \(\times\) 125 cm \(\times\) 9 cm, which was 0.37\(\lambda\) 0.37\(\lambda\) \(\times\) 0.026\(\lambda\) in terms of the wavelength at 88 MHz. The measured results showed that the reflection coefficient was \(-\)12.9 dB at the resonant frequency 86 MHz, which was slightly shifted from the simulation result but showing an overall good agreement. The S11's \(<\)\(-\)10 dB bandwidth was 83.3-86.1 MHz. The measured azimuth radiation pattern was also similar to the simulated one, but its level was 0.5 dB lower. However, the omnidirectional radiation could be observed in spite of the short height of 9 cm (0.026\(\lambda\) at 88 MHz). The proposed antenna can be a candidate to substitute a long monopole antenna at the VHF frequency range at the price of a large mounting area.  
Conceptualization, J.-Y.C.; methodology, J.-Y.C.; simulation and measurement, G.Y.; writing--original draft preparation, J.-Y.C.; writing--review and editing, J.-Y.C. and G.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Future Combat System Network Technology Research Center program of Defense Acquisition Program Administration and Agency for Defense Development (UD190033 ED).  
Not applicable.  
Figure 12: Measured received power and simulated antenna gain at \(\theta=90^{\circ}\) and \(\phi=270^{\circ}\).  
Figure 13: Measured power pattern at 86 MHz and simulated antenna gain pattern at 88 MHz.
# Cryptographic Protocol with Keyless Sensors Authentication for WBAN in Healthcare Applications
## 3 Protocol Design and Description
### Description of the Protocol Phases
#### 3.3.3 Phase 2--Sensing, Encryption, and Sending of Information  
After the devices in the WBAN initiate communication and register, the C-sensors can start their function. The information is calculated per unit time, \(m_{i}\), to form the final message, \(M_{MP}\), a data set such that \(M_{MP}=m_{0},m_{1},m_{2},m_{3},\ldots,m_{n}\). When complete, it is sent to B. \(K_{s}\) is used to encrypt the information in \(M_{MP}\) using the encryption algorithm \(E_{k}\) that obtains the encrypted message \(C_{m}\) that is sent from B to A. Figure 7 shows the diagram of phase 2, which shows the information path and the functions used in this phase.
## 3 Results  
### Quantitative Analysis of ESs  
Over the past 23 years, the ESs have exhibited significant fluctuations, with their spatial distribution showing marked heterogeneity. FP (slope = 0.034, \(p<0.01\)) and CS (slope = 0.654, \(p<0.01\)) both demonstrated a highly significant increasing trend, while HQ showed a significant declining trend (slope = \(-0.0008,p<0.01\)). SC (slope = \(-0.143\), \(p=0.537\)) also exhibited a significant decrease. Although WY (slope = 0.842, \(p>0.05\)) showed a slight upward trend, the change was not statistically significant (Figure 2). The responses of different ecosystem services to environmental changes may differ significantly, likely due to various factors, including but not limited to external disturbances and the complexity of ecosystem responses. The spatial distribution patterns revealed that HQ and CS shared similar high-value areas located in the east and north. High-value SC areas were concentrated near the Nanling Mountains. The high-value areas of FP were found in the central and western parts, and the rest of the regions were evenly distributed. WY exhibited a spatial distribution with higher values in the central and eastern parts and lower values in the western parts, likely due to variations in the terrain altitude. The western plateau mountains have less precipitation and higher surface runoff.  
Figure 2: The changes in ESs from 2000 to 2022. (**a**) The mean distribution of ESs over the 23-year period; (**b**) the changes from 2000 to 2010; (**c**) the changes from 2010 to 2022, where the curve represents the frequency distribution; and (**d**) the mean values of the ESs and the fitted trend lines.  
From 2000 to 2010, the total WY in the study area changed slightly, with large spatial differences: a marked increase occurred in the east, and decrease occurred in the central part. After 2010, the decline shifted westward, resulting in reduced increases and only minor gains along the eastern edge. WY declined overall during this period. From 2000 to 2010, SC increased in the Nanjing region; however, subsequent years saw significant changes across the entire area, ultimately leading to an overall decline. The HQ values were generally high throughout the region, with an overall distribution predominantly exceeding 0.7. There was a slight decrease from 2000 to 2010, followed by minimal changes thereafter. FP was mainly concentrated in flatter areas surrounding urban regions and exhibited a dispersed distribution. High CS values were mainly distributed in mountainous areas, which is related to the distribution of forests. The increase in CS from 2000 to 2010, followed by a more pronounced increase, is possibly linked to China's ecological protection policies and initiatives, such as aforestation.
# Void Hole Avoidance for Reliable Data Delivery in IoT Enabled Underwater Wireless Sensor Networks
## 6 Simulation Results
### Performance Comparison
#### 6.3.6 Network Lifetime  
Network lifetime is shown in Figure 12, which depends on the utilization of the node battery; if the dissipation of the node's available power is efficient, then it will perform the network operation for a longer time. B-DBR leads in the lifetime plot compared to all the schemes. This scheme has a fall back recovery mechanism to ensure the data reach the destination. However, it does not find the data  
Figure 11: Comparison of alive nodes.  
route all the time and drops the data packet; that is the reason it has a greater drop ratio than other proposed schemes, as given in Figure 13.  
The WDFAD-DBR and RE-PBR have a greater drop ratio, but still, the lifetime is shorter, because, in-spite of dropping packets, the same node is elected again and again, until its death. The immutable destination node selection is due to the assignment of weight to forwarder nodes based on the link and depth difference in RE-PBR and WDFAD-DBR, respectively.  
On the other hand, the other proposed schemes (A-DBR, CA-DBR and C-DBR) show almost the same behavior throughout the network lifespan. A-DBR has lower performance due to the adjustment of the transmission range, which consumes more battery and leads to a shorter network lifetime; while CA-DBR picks a collision-free path, which means a route with good quality gets selected time and again, leading to the sudden death of the intermediate nodes and creating a void hole. Thus, it reduces the network lifetime. On the other hand, C-DBR creates clusters to gather data packets locally and transmits a composite packet towards the destination. Thus, the repeated selection of a high energy node leads to the quick depletion of the node's battery.