esa-sceva/satcom-chunk-collection huggingface dataset:
# Distributed Bragg Reflectors for GaN-Based Vertical-Cavity Surface-Emitting Lasers
## 4 Non-Epitaxial DBRs for GaN VCSELs
### Non-Epitaxial DBRs through Epitaxial Lateral Overgrowth (ELO)  
Epitaxial lateral overgrowth (ELO) was a technique used to grow GaN over patterned dielectric mask regions which is effective in dislocation reductions [89]. The idea of utilizing dielectric DBR stacks as ELO mask layers for VCSEL fabrication was first proposed by [PERSON] and [PERSON] [90]. Sony Corporation reported in 2015 the use of ELO to fabricate double dielectric VCSELs without substrate removal [54; 91]. The fabrication of the VCSEL by the ELO technique starts with a dielectric DBR deposition and patterning on a GaN template. Subsequently, ELO is conducted by MOCVD to grow the remaining VCSEL structure (see Figure 9), and the dielectric DBR is buried by the overgrown GaN layers. Finally, another dielectric DBR is deposited on the top side to form the full microcavity. In spite of the elegance of this approach, there is a practical limitation in the minimum thickness of the microcavity. Given the limited achievable ratio between lateral and vertical growth rates (seldom exceeds 4) in ELO [92], the formation of VCSEL aperture area of greater than 20 or 30 \(\upmu\)m for practical fabrication produces correspondingly a cavity of more than 5 or even 10 \(\upmu\)m in thickness, thus precluding the possibility of short-cavity, single (longitudinal) mode operation. Another drawback for a long planar VCSEL cavity is an increased diffraction loss, amounting to greater than 1% per round trip for a 10 \(\upmu\)m cavity length [54]. Additionally, the uniformity of ELO growth over the entire wafer, for photonic purposes, is still not established and could be a concern.  
Figure 8: (**a**) Cross-sectional schematic of nonpolar m-plane GaN VCSEL with flip-chip design and dielectric mirrors. (**b**) Scannng electron microscope (SEM) image of multiple completed devices. (**c**) Optical microscopy image of device lasing under pulsed operation [84].
## 3 DASH Interferometer Mount
### Design of Structural Frame
#### 3.2.2 Design of the Column  
The vibration modes of the FEA are the optical elements rotating around the Y-axis, \(X\)-axis, and \(Z\)-axis, respectively, because the optical element of the DASH interferometer assembly has an asymmetric structure. A column configuration was used to improve the stiffness of the three degrees of freedom. As shown in Figure 6, there are three types of columns: Column I (Column III), Column II, and Column IV. Columns I and II are used to increase the rotational stiffness around the \(Y\)-axis, columns II and III are used to increase the rotational stiffness around the \(X\)-axis, and columns I, II, and III are used to increase the rotational stiffness around the \(Z\)-axis. Moreover, the incident and exit beams are on both sides of column IV. Therefore, column IV is straight, as illustrated in Figure 6.  
In the FEA, the columns are simplified into simply supported beam structures, and the slope of each column is proportional to the force it would bear. The optimal structure is designed using FEA, i.e., by analyzing the natural frequencies of the structural frame with different column slopes. Figure 7 shows the relationship between the slope angle and natural frequencies in the three directions. It could be noted that the relative change in the natural frequency decreased when the slope was 57\({}^{\circ}\), which has not been discussed in existing methods available in the literature. Therefore, the slope of Columns I, II, and III was proposed to be 57\({}^{\circ}\).  
Figure 5: Structural schematic: (**a**) interferometer cover; (**b**) interferometer base.  
Figure 6: Diagram of columns.
###### Abstract  
This paper presents a low-profile dual-band antenna with directional radiation characteristics for wireless local area network (WLAN) applications. The proposed directional antenna is composed of a coupling microstrip line, two F-shaped strips, two rectangular strips, and a defected ground plane. The measured impedance bandwidth of the proposed antenna is 180 MHz (2.33-2.51 GHz) and 830 MHz (5.09-5.92 GHz), which can cover Institute of Electrical and Electronic Engineers (IEEE) 802.11 a/b/g frequency bands. The dual-band antenna exhibits a desirable directional radiation patterns in the vertical and horizontal planes with the peak gain of 6.55 dBi in the lower frequency band and 8.1 dBi in the higher frequency band. The measured antenna efficiency is 70% at 2.4 GHz and 84.5% at 5.5 GHz. The proposed dual-band WLAN station antenna is designed on a FR4 substrate with overall dimensions of 69 mm \(\times\) 50 mm \(\times\) 1.6 mm.  
dual-band; directional radiation; microstrip line; directional antenna +
Footnote †: journal: Applied Sciences  
narrowband antenna, it is time-consuming to design a dual-band Wi-Fi Yagi array. Quasi-Yagi antennas [10; 11; 12] can increase the impedance bandwidth, but the gain performance is not as good as that of the Yagi antenna. In general, it is a challenge to design a wideband and compact Yagi antenna. Slot antennas are often used as high-gain directional antennas. A multiband antenna with directional radiation can be obtained by loading slots on the radiator. Currently, slot antennas have been widely studied as directional antennas [13; 14]. A directional slot antenna with a squared arch structure is presented to achieve impedance matching, high gain, and vertical polarization in dual band Wi-Fi unidirectional antenna design [13]. A slot in a ground plane with a complimentary microstrip stub was exploited to design a high-gain directional antenna with high radiation efficiency [14]. Based on the characteristics of the slot antenna, a slot-loop antenna, equivalent to a dipole array, is proposed, showing high-efficiency directional characteristics [15]. The size of the slot loop antenna is smaller than that of the Yagi antenna, and it is a suitable candidate as a high gain directional antenna.  
In this paper, a dual-band antenna with directional radiation patterns is presented. Two symmetrical rectangular strips and F-shaped strips are introduced into the ground layer to form good impedance matching, thereby realizing a dual-band antenna. A large ground plane and a microstrip line are combined to form a high-gain directional antenna. The size of the dual-band antenna is \(50\times 69\times 1.6\) mm\({}^{3}\) with a gain of 6.55 dBi and 8.1 dBi at two resonant frequencies, respectively. The proposed dual-band antenna exhibits good directional radiation characteristics for wireless local area network (WLAN) applications.
## 2 System Model
### Detection and Tracking Algorithm  
The flow chart for the QR inspired LEA detection is represented in Figure 7. The steps involved in the detection process are given below.  
1. Acquired the image using the camera attached at the front end of the secondary vehicle.
2. Convert the captured color image into a grayscale image.
3. Use a median filter to remove the noise effect created by the ambient light.
4. Convert the filtered grayscale image into a binary image.
5. The binary image is scanned for the finder pattern of the QR-Code with a pattern scan ratio of 1:1:3:1:1 throughout the image. The respective center points of the three finder pattern are obtained by scanning. Similarly, the alignment pattern is determined with the pattern scan ratio of 1:1:1:1, and the center point of the alignment pattern is calculated.
6. Finder and alignment patterns are tracked using a particle filter
7. Identify the coordinates of the ROI corresponding to the LEA using the finder patterns and alignment pattern.
8. Select the ROI on the grayscale image from Step 2 and perform a geometric transform to retranslate the image to the original dimension.  
For Each LED Do,  
1. Extract the ROI of each LED from the restored image.
2. Find the average of the pixel values inside the ROI of the LED.
3. Adaptive threshold technique to estimate the status of the LED.  
End  
1. Set the incoming message bit based on the status of the LED from Step 11.  
Figure 6: Synthetic color image.  
The flow chart for the particle filter tracking is represented in Figure 8. The steps involved in tracking are given below.  
1. Initialize the particles for three finders and one alignment pattern of the QR-code.
2. Sample these particles in the captured image for three finder patterns and alignment pattern. Initially, the particles are spread randomly over the entire image, indicating the possible location of the QR pattern.  
Figure 7: Flow chart of QR-Code Detection.  
* Apply importance filtering based on the identified pattern scan ratios of three finder patterns and alignment pattern. Importance filtering calculates the weight of all the particles and selects the region that contains particles with large weights.
* Resample the particles inside the estimated regions of finder patterns and alignment pattern as in Figure 9.
* Estimate three finder patterns and alignment pattern locations from the sampled particles.
* Select the next captured image and repeat from step 2 to step 5 to estimate three finder patterns and alignment pattern.
## 4 Proposed Algorithm
### Heterogeneous Network Energy Dissipation Model
When data is aggregated, the energy required is represented by \(E_{DA}\). The symbol \(l\) represents a data packet. For long-distance transmission to a base station, denoted by \(d_{toBS}^{4}\), the transmitter amplifier energy is denoted as \(\epsilon_{np}\). And the energy consumed by non-cluster heads is represented by \(E_{n-CH}\):  
\[E_{n-CH}=l\cdot E_{elec}+l\cdot\epsilon_{fs}\cdot d_{toCH}^{2} \tag{10}\]  
In a given context, the symbol \(l\) represents a packet of data, \(E_{elec}\) is the energy used by the transmitter, and \(\epsilon_{fs}\) represents a transmitter amplifier in a free state at a short distance from the cluster head \(d_{toCH}^{2}\). The constant value 0.765, as derived in reference [25], is used in the following expression. \(M\) and \(n\) represent the area of the sensor and the total number of nodes, respectively, \(d_{toCH}^{2}\) signifies the short distance to the cluster head, and \(d_{toBS}^{4}\) represents the long distance to the base station.
# FDCNet: Frontend-Backend Fusion Dilated Network Through Channel-Attention Mechanism
## 1 Introduction
Avoiding the crude concatenate, the channel-attention block could take the weights of channels into consideration and ensure consummate fusion by enhancing the various scale features. On the other hand, it can conduct feature recalibration to capture spatial correlations and selectively emphasize informative features, so as to make the density map present a smooth transition between nearest pixels and improve the representation of our network. * We utilize the dilated layer as the dilated block to the tail end of the network, which has less parameters but expands the receptive field. Furthermore, it contains more detailed global and spatial information to generate a high-quality density map. * We subjoin SSIM to the loss function, which measures the local pattern consistency of the estimated density map and ground truth. So the final loss function has better representation of the difference between the estimation and the ground truth. Owing to it the accuracy can be greatly improved.
# In Situ Measurement of Alkali Metals in an MSW Incinerator Using a Spontaneous Emission Spectrum
###### Abstract  
This paper presents experimental investigations of the in situ diagnosis of the alkali metals in the municipal solid waste (MSW) flame of an industrial grade incinerator using flame emission spectroscopy. The spectral radiation intensities of the MSW flame were obtained using a spectrometer. A linear polynomial fitting method is proposed to uncouple the continuous spectrum and the characteristic line. Based on spectra processing and a non-gray emissivity model, the flame temperature, emissivity, and intensities of the emission of alkali metals were calculated by means of measuring the spectral radiation intensities of the MSW flame. Experimental results indicate that the MSW flame contains alkali metals, including Na, K, and even Rb, and it demonstrates non-gray characteristics in a wavelength range from 500 nm to 900 nm. Peak intensities of the emission of the alkali metals were found to increase when the primary air was high, and the measured temperature varied in the same way as the primary air. The temperature and peak intensities of the lines of emission of the alkali metals may be used to adjust the primary airflow and to manage the feeding of the MSW to control the alkali metals in the MSW flame. It was found that the peak intensity of the K emission line had a linear relationship with the peak intensity of the Na emission line; this correlation may be attributed to their similar physicochemical characteristics in the MSW. The variation trend of the emissivity of the MSW flame and the oxygen content in the flue gas were almost opposite because the increased oxygen content suppressed soot formation and decreased soot emissivity. These results prove that the flame emission spectroscopy technique is feasible for monitoring combustion in the MSW incinerator in situ.  
MSW incinerator; flame emission spectroscopy; flame temperature; in-situ measurement +
Footnote †: journal: Applied Physics Letters  
_Article_  
In Situ Measurement of Alkali Metals in an MSW  
Incinerator Using a Spontaneous Emission Spectrum
# Resource Mapping Allocation Scheme in 6G Satellite Twin Network
## 6 Results and Discussions
The control information transmission time of serverless architecture is less than that of parallel architecture due to the means of decentralized control, so the serverless architecture consumes the least simulation time.
## 2 Results and Discussion
### Lipophilicity and Protein Affinity
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**No.** & **HBDH** & **M log \(P\)** & **Clog \(P\)** & **ACD log \(P\)** & **tPSA** & **CMR** & **pK\({}^{\text{a}}\)** \\ \hline
**8a** & 2 & 0.435 & 0.299 & 1.46 & 132.12 & 10.27 & 1.01 \\
**8b** & 2 & 0.435 & 0.299 & 1.46 & 132.12 & 10.27 & 1.01 \\
**8c** & 2 & 0.905 & 1.227 & 2.34 & 132.12 & 11.19 & 1.01 \\
**8d** & 2 & 0.905 & 1.227 & 2.34 & 132.12 & 11.19 & 1.01 \\
**8e** & 2 & 0.435 & 0.299 & 1.46 & 132.12 & 10.27 & 1.01 \\
**8f** & 2 & 0.435 & 0.299 & 1.46 & 132.12 & 10.27 & 1.01 \\
**8g** & 3 & \(-\)0.289 & \(-\)0.157 & 0.50 & 152.35 & 10.42 & 1.01 \\
**8h** & 3 & \(-\)0.289 & \(-\)0.157 & 0.50 & 152.35 & 10.42 & 1.01 \\
**8i** & 2 & 1.132 & 1.755 & 2.87 & 132.12 & 11.66 & 1.01 \\
**8j** & 2 & 1.132 & 1.755 & 2.87 & 132.12 & 11.66 & 1.01 \\
**8k** & 3 & \(-\)0.051 & 0.248 & 1.28 & 152.35 & 10.88 & 1.01 \\
**8l** & 2 & 0.672 & 0.698 & 1.81 & 132.12 & 10.73 & 1.01 \\
**8m** & 0 & 0.905 & 1.122 & 2.27 & 102.54 & 11.23 & 1.01/5.96 \\ \hline \hline \end{tabular} \({}^{\text{a}}\) HBDH = the number of hydrogen bond donor protons; MlogP = the Moriguchi estimation log \(P\); C log \(P\); ACD log \(P\) = log \(P\) calculated using the ACD/Labs algorithm; tPSA = the polar surface area; CMR = the molar refractivity; and pK\({}_{\text{a}}\) = log K\({}_{\text{a}}\).  
\end{table} 
Table 6: The molecular descriptors (\({}^{\text{a}}\)) of the compounds calculated in silico.
## 3 Results
### Allele Frequency Studies in the Cases and Controls of the Thrombophilic Variants  
The allele frequencies present in the G1691A, G20210A and C677T SNPs are shown in Table 5. The level of risk alleles (A allele) found in G16191A was 3% and 0.5% in both the FI  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Gene (rsnumber)** & **Genotypes** & **Infertile (\(\mathit{t}\) = 100)** & **Fertile (\(\mathit{t}\) = 100)** & **OR (95\%CI)** & \(\mathit{p}\)**-Value** \\ \hline \multirow{8}{*}{_FVL_ (rs6020)} & GG & 95 (95\%) & 99 (99\%) & 1 Reference & 1 Reference \\ \cline{2-6}  & GA & 04 (04\%) & 01 (01\%) & 4.168 (0.45–37.97) & 0.171 \\ \cline{2-6}  & AA & 01 (01\%) & 00 (00) & 2.084 (0.06–62.84) & 0.665 * \\ \cline{2-6}  & GA + AA vs. GG & 05 (05\%) & 01 (01\%) & 5.211 (0.59–45.42) & 0.097 \\ \cline{2-6}  & AA + GG vs. GA & 96 (96\%) & 99 (99\%) & 0.242 (0.02–2.21) & 0.174 \\ \cline{2-6}  & GG + GA vs. AA & 99 (99\%) & 100 (100\%) & 0.495 (0.016–14.92) & 0.679 * \\ \hline \multirow{8}{*}{_PT_/_FII_ (rs20210)} & GG & 93 (93\%) & 98 (98\%) & 1 Reference & 1 Reference \\ \cline{2-6}  & GA & 06 (06\%) & 02 (02\%) & 3.161 (0.62–16.06) & 0.144 \\ \cline{2-6}  & AA & 01 (01\%) & 00 (00) & 2.108 (0.06–63.55) & 0.661 * \\ \cline{2-6}  & GA + AA vs. GG & 07 (07\%) & 02 (02\%) & 3.688 (0.74–18.21) & 0.088 \\ \cline{2-6}  & AA + GG vs. GA & 94 (94\%) & 98 (98\%) & 0.319 (0.06–1.62) & 0.149 \\ \cline{2-6}  & GG + GA vs. AA & 99 (99\%) & 100 (100\%) & 0.495 (0.01–14.92) & 0.679 * \\ \hline \multirow{8}{*}{
\begin{tabular}{c} MTHFR \\ (rs1801133) \\ \end{tabular} } & CC & 74 (74\%) & 72 (72\%) & 1 Reference & 1 Reference \\ \cline{2-6}  & CT & 21 (21\%) & 26 (26\%) & 0.785 (0.40–1.52) & 0.473 \\ \cline{1-1} \cline{2-6}  & TT & 05 (05\%) & 02 (02\%) & 2.432 (0.45–12.94) & 0.284 \\ \cline{1-1} \cline{2-6}  & CT + TT vs. CC & 26 (26\%) & 28 (28\%) & 0.903 (0.48–1.68) & 0.751 \\ \cline{1-1} \cline{2-6}  & TT + CC vs. CT & 79 (79\%) & 74 (74\%) & 1.322 (0.68–2.54) & 0.404 \\ \cline{1-1} \cline{2-6}  & CC+ CT vs. TT & 95 (95\%) & 98 (98\%) & 0.387 (0.07–2.04) & 0.249 \\ \hline \hline \end{tabular}  
* * Yates correction between the genotypes.  
\end{table}
Table 4: Genotype frequencies of the thrombophilic variants in infertile and fertile women.  
cases and controls, 4% and 1% in the cases and controls of the G20210A SNP and 15.5% and 15% in the T alleles of the C677T SNP. However, the normal allele (G allele) level was found to be 97% and 99.5% in the G6191A SNP, 96% and 99% in the G20210A SNP and, finally, 84.5% and 85% in the C677T SNP. The statistical analysis conducted between the FI cases and controls and analysis showed results as follows: G1691A (OR-6.155 (95%CI: 0.73-51.59); \(p\) = 0.056), G20210A (OR-4.125 (95%CI: 0.86-19.67); \(p\) = 0.054) and C677T (OR-1.039 (95%CI: 0.60-1.79); \(p\) = 0.889).
## 5 Using MEC to Augment the Computing Capabilities of Vehicles  
Mobile devices in IoV networks leverage collected real-time data and information to perform intensive computations and address latency-sensitive tasks. This type of network aims to make an optimal and smart decision. However, due to the limited computational capacity and storage of mobile devices, some computations cannot be completed within prespecified time limits, which may lead to problems such as traffic congestion. To solve these problems, cloud computing has been proposed to support the execution of tasks in the cloud, which can lead to information loss [95]. Recently, researchers have introduced MEC to support low-delay, low-energy task processing using some available Edge Points (EPs) near vehicle users [59].
## 6 Result and Discussion
### Cumulative Ductility Factor (CDF)  
Figure 19 shows the increase in cumulative ductility factor (CDF) for the selected BRBs corresponding to the five sets of the multi-hazard scenarios. In the figure, the horizontal lines mark the CDF criteria according to Equation (18) for the certain \(R\) factors (\(R\) = 1, 2, 3, and 4, \(CDF\) = 75, 150, 225, and 300) to measure the progressive damage of selected BRB in terms of CDF. In contrast to the CDI and PSE, it is observed that all the selected BRBs are reaching their maximum capacity in terms of cumulative ductility factor. The effective age of the BRBs (with \(R\) = 4) is about 40-75 years corresponding to the BRB's location in the high-rise building and average strain amplitude. On the other hand, the CDF capacity of the BRBs (with \(R\) = 1) is about 20 years, regardless of the average strain amplitude and damper location. Comparing the results of CDI and PSE with the cumulative ductility factor, it is important to point out that the BRBs that satisfy the CDI and PSE indexes are severely prone to progressive damage in terms of CDF. Therefore, the current study recommends  
Figure 17: Cumulative damage index (CDI) of selected BRBs under multi-hazard scenarios.  
precisely evaluating the fatigue life BRBs under different multi-hazard scenarios in terms of different fatigue criteria.  
Figure 19: Cumulative ductility factor (CDF) of selected BRBs under multi-hazard scenarios.  
Figure 18: Plastic strain energy (PSE) of selected BRBs under multi-hazard scenarios.
## 7 Results and Discussion
### PCA and MRE Performance Comparison with HMM and Proposed EHMM
\begin{table}
\begin{tabular}{c c c c c c} \hline \(\chi\) (\%) & **Predictor** & \(\mathcal{S}_{e}\) (\%) & \(\mathcal{S}_{p}\) (\%) & \(\mathcal{P}\) (\%) & \(\mathcal{F}1\) (\%) \\ \hline
70 & MRE-EHMM & 99.68 & 99.70 & 99.62 & 90.65 \\  & PCA-EHMM & 99.69 & 99.70 & 99.63 & 99.66 \\  & RF & 97.01 & 97.09 & 96.98 & 97.00 \\  & LR & 96.77 & 96.79 & 96.65 & 96.71 \\  & DT & 96.23 & 96.25 & 96.10 & 96.17 \\  & KNN & 95.87 & 95.86 & 95.62 & 95.75 \\ \hline
75 & MRE-EHMM & 99.80 & 99.80 & 99.72 & 99.76 \\  & PCA-EHMM & 99.79 & 99.82 & 99.71 & 99.75 \\  & RF & 97.22 & 97.24 & 97.19 & 97.16 \\  & LR & 96.99 & 96.97 & 96.88 & 96.94 \\  & DT & 96.44 & 96.43 & 96.21 & 96.32 \\  & KNN & 96.04 & 96.03 & 95.89 & 95.96 \\ \hline
80 & MRE-EHMM & 99.81 & 99.81 & 99.73 & 99.77 \\  & PCA-EHMM & 99.81 & 99.82 & 99.72 & 99.77 \\  & RF & 97.56 & 97.54 & 97.29 & 97.42 \\  & LR & 97.11 & 97.09 & 97.00 & 97.06 \\  & DT & 96.88 & 96.89 & 96.78 & 96.83 \\  & KNN & 96.54 & 96.57 & 96.48 & 96.38 \\ \hline \end{tabular}
\end{table} 
Table 5: MRE-EHMM and PCA-EHMM performance comparison with other ML models.
## 4 Experiments and Results
### Basic Settings
#### 4.1.2 Implementation Details  
In this paper, the proposed PIANet was implemented by using PyTorch and Python on an NVIDIA Geforce RTX 3090 with 24 GB memory, and the learning rate and batch size were set to 0.001 and 16, respectively. The hyperparameters were set as follows: both \(w_{1}\) and \(w_{2}\) were initialized to 0.5, and \(\alpha=15\), \(\beta=15\), \(\lambda_{1}=3\), \(\lambda_{2}=3\), \(\lambda_{3}=9\), \(r=2\). Moreover, the Adam optimizer was used to optimize the parameters in the proposed PIANet. The early stopping was used as the training stopping criterion to avoid overfitting, that is, the best validation RMSE was recorded in each epoch, and the training did not stop until the best validation RMSE rose 10 times in a row. All convolutional layers were followed by the batch normalization layers and leaky ReLu layers. Furthermore, the historical OD matrices of previous days and the historical inflow series of the same day were preprocessed by Min-Max normalization to avoid gradient explosion before feeding into the architecture of the proposed PIANet.
# AI Training Data Management for Reliable Autonomous Vehicles Using Hashgraph
## 3 Blockchain and Hashgraph
In IoT deployments, its lightweight framework supports secure data validation across distributed sensor nodes with limited computational capabilities. By enabling parallel transaction processing, the DAG-based architecture reduces latency and enhances system performance. The consensus algorithm of hashgraph facilitates asynchronous agreement among network participants, maintaining consistency and reliability even under high transactional loads. This combination of parallelism and consensus provides an efficient framework for distributed data management. Due to its minimal latency and excellent scalability, hashgraph is well suited for latency-sensitive applications such as autonomous vehicle networks, large-scale IoT systems, and ad hoc mobile communications. Its resource-efficient design facilitates deployment in constrained environments while ensuring secure and consistent data synchronization.