esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Sound Sampling and Data Transmission
### Network Topology  
The HWASN implemented in this study (as shown in Figure 1) consists of three SLTB004A-based sensor nodes, two ESP32-based relay nodes, one ESP32-based bridge node, and a Jetson Nano-based central processing node. Each sensor node samples sound signals and transmits them to its respective relay node via Bluetooth Low Energy (BLE). The relay nodes then forward the data through a WiFi Mesh network to the bridge node, which relays it to the Jetson Nano for further analysis. The central processing includes calculating the A-weighted SPL and classifying the sound types before uploading the data to a cloud server. The two relay nodes are configured in a chained connection to verify the network's multi-hop capability, ensuring data from the furthest relay node is routed through the nearest relay node to the central processor. Packet ID monitoring allows for assessing packet loss, providing insights into network performance and reliability.
# Point Cloud Registration Algorithm Based on Adaptive Neighborhood Eigenvalue Loading Ratio
## 2 Methods
### Point Cloud Registration Algorithm Based on Feature Point Similarity Formula
After matched-pair optimization, all point clouds are extracted based on their corresponding index point numbers, which are subjected to the sample consensus initial registration (SAC-IA) algorithm for coarse registration. Although good registration poses can be achieved, the SAC-IA algorithm's registration results do not yet meet the final requirements. Therefore, it is necessary to use the iterative closest point (ICP) algorithm for the fine registration of the source point cloud. The traditional ICP algorithm, when dealing with large point clouds, performs a nearest neighbor search that may require traversing all points in the point cloud, resulting in a linear computational complexity related to the time complexity [27] of O(NM). To address the issue, in this study, the KD-tree is adopted to accelerate the nearest neighbor search, thereby reducing the time complexity to O(log N) or O(log M), which significantly improves the time efficiency of fine registration.
# Effective 5G Wireless Downlink Scheduling and Resource Allocation in Cyber-Physical Systems +
## 6 Conclusions and Future Work  
The upcoming 5G technology is envisioned to support diverse use-cases such as enhanced Mobile Broadband (eMBB), enhanced Machine Type Communication (eMTC), and Ultra-Reliable Low Latency Communication (URLLC). These use cases may generate different types of traffic at the base station. The first generation of 5G comes with backward LTE compatibility, which generates even more diversity in traffic at the base station. Hence, we need an effective Scheduling and Resource Allocation (SRA) at the 5G base station. In this paper, we have proposed a new SRA algorithm based on dynamic programming that can accommodate diverse use-cases of 5G and make effective SRA decisions. The simulation results show the robust performance of the proposed SRA for the two dominant waveforms for 5G, i.e., Orthogonal Frequency Multiplexing (OFDM) and Filter Bank Multi-Carrier (FBMC). For LTE, our SRA method outperforms the advanced greedy SRA algorithm [13] by up to approximately 60%, 2.6% and 1.6% in terms of goodput, goodput fairness and delay fairness, as observed in [17]. In the case of URLLC and eMTC, our SRA algorithm continues to outperform [13] by up to 17.24%, 18.1%, 2.5% and 1.5% in terms of average goodput, correlation impact, goodput fairness and delay fairness. In the future, we will explore other 5G New Radio (NR) waveforms and leverage them in our SRA framework. We will also investigate if the dynamic programming algorithm for SRA can be further extended to support V2X communication, which is significantly more challenging due to high-speed mobile stations.  
[PERSON] has developed the dynamic programming algorithm for 5G downlink scheduling and resource allocation. [PERSON] also performed the simulation study. [PERSON]-[PERSON] helped [PERSON] to design the algorithm and discussed the results with [PERSON]. [PERSON]-[PERSON] also helped [PERSON] to write this paper.  
This work was supported, in part, by NSF Grant CNS1526932.  
We appreciate the guest editor and anonymous reviewers for their help to improve this paper.  
The authors declare no conflict of interest.
# A Review of Fault Diagnosis Methods for Key Systems of the High-Speed Train
###### Abstract  
High-speed train is a large-scale electromechanical coupling equipment with a complex structure, where the coupling is interlaced between various system components, and the excitation sources are complex and diverse. Therefore, reliability has become the top priority for the safe operation of high-speed trains. As the operating mileage of high-speed trains increases, various key systems experience various degrees of performance degradation and damage failures. Moreover, it is accompanied by the influence of external environmental high interference noise and weak early fault information. Thus, those factors are serious challenges for the condition monitoring and fault diagnosis of high-speed trains. Therefore, this paper summarizes the research progress and theoretical results of the fault detection, fault isolation, and fault diagnosis methods of the key systems of high-speed trains. Finally, the paper summarizes the applicability of the main methods, discusses the challenges and opportunities of condition monitoring and fault diagnosis of high-speed trains, and looks forward to improving its diagnosis level.  
high-speed train; key systems; fault diagnosis; feature extraction; pattern recognition +
Footnote †: journal: Applied  
0000-0001-5000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-0000-00000-0000-00000-00000-00000-00000-00000-00000-00000-00000  
is extremely complex. Hence, the collected signals are not only independent but also have a certain correlation and often contain more interference components. To this end, this thesis summarizes the fault diagnosis techniques of key systems (bogie system, traction/braking system, and electrical/information control system) of high-speed trains in recent years, as shown in Figure 1. From Figure 1, signal processing, machine learning, and deep learning techniques are effective methods to achieve condition monitoring and fault diagnosis of each key system. Finally, the development of high-speed train fault diagnosis technology is forecasted. The main contributions of this study are as follows:  
1. The fault diagnosis methods of the bogie system of the high-speed train are presented.
2. The fault diagnosis methods of the traction system and brake system of the high-speed train are summarized.
3. The fault diagnosis methods of electric systems and information control systems of the high-speed train are overview.
4. The applicability of main fault diagnosis methods for high-speed trains is discussed.
# Low-Cost 400 Gbps DR4 Silicon Photonics Transmitter for Short-Reach Datacenter Application
###### Abstract  
Targeting high-speed, low-cost, short-reach intra-datacenter connections, we designed and tested an integrated silicon photonic circuit as a transmitter engine. This engine can be packaged into an optical transceiver module which meets the QSFP-DD Form Factor, together with other electrical/optical components. We first present the design and performance of a high-speed silicon modulator, which had a 3-dB EO bandwidth of \(\approx\)40 GHz and an ER of \(\sim\)5 dB. We then incorporated the engine onto a test board and injected a 53.125 Gbaud PAM4 signal. Clear eye patterns were observed at the receiver with TDEQ \(\sim\)3 dB for all four lanes.  
datacom; integrated optics devices; silicon photonics; optical modulator; transmitter; PAM-4; QSFP-DD; 400 GBASE-DR4 +
Footnote †: journal: Antimed.  
0000-0002-0001-9410][PERSON]  
0000-0002  
high data rates [21]; therefore, a slightly more complex DSP must be used, especially for longer-distance communication. EMLs have excellent performance in terms of bandwidth and insertion loss (IL), but the cost of introducing EML is relatively high and it also needs to be designed carefully for thermal stability [22]. Silicon photonics is also a low-cost, high-performance solution. However, due to the inherent high IL, it is usually used to cover 500 m to 2 km, the'mid-range' of the intra-datacenter links, whereas DMLs/EMLs are often seen in 2 km to 10 km or even 20 km links.  
In our previous work, we experimentally demonstrated a 400 Gb/s transmitter with a silicon photonics engine [23]. Due to insufficient modulator bandwidth, we applied shaping, pre-compensation and a peak-to-peak differential voltage as high as 5 V\({}_{\text{ppd}}\) at the transmitter side. When receiving the optical signal, we had to apply offline DSP with a digital square and filtering algorithm for timing recovery and a least mean square algorithm, with a 21-tap filter, in order to balance the complexity with system performance. However, after we redesigned the modulator, doubled its bandwidth and optimized our other silicon photonics components, we confirmed successful 400 Gb/s signal transmission in our new silicon photonics engine that met the IEEE standard.  
In this paper, we first illustrate a technical roadmap for building a low-cost silicon photonic transceiver with MACOM devices. Then, we discuss the design and performance of our modulator. After that, we present the measurements of a 400 Gb/s DR4 transmitter when we attach the silicon photonics engine to an evaluation board. Finally, we summarize the results of our current product and show plans for test and design in future.
# An Improved Detail-Enhancement CycleGAN Using AdaLIN for Facial Style Transfer
###### Abstract  
The rise of comics and games has led to increased artistic processing of portrait photos. With growing commercial demand and advancements in deep learning, neural networks for rapid facial style transfer have become a key research area in computer vision. This involves converting face photos into different styles while preserving content. Face images are more complex than regular images, requiring extensive modification. However, current methods often face issues such as unnatural color transitions, loss of detail in highlighted areas, and noticeable artifacts along edges, resulting in low-quality stylized images. In this study, an enhanced generative adversarial network (GAN) is proposed, which is based on Adaptive Layer Instance Normalization (AdaLIN) + Laplacian. This approach incorporates the AdaLIN normalization method, allowing for the dynamic adjustment of Instance Normalization (IN) and Layer Normalization (LN) parameters' weights during training. By combining the strengths of both normalization techniques, the model selectively preserves and alters content information to some extent, aiming to strike a balance between style and content. This helps address problems such as unnatural color transitions and loss of details in highlights that lead to color inconsistencies. Furthermore, the introduction of a Laplacian regularization term aids in denoising the image, preventing noise features from interfering with the color transfer process. This regularization also helps reduce color artifacts along the face's edges caused by noise while maintaining the image's contour information. These enhancements significantly enhance the quality of the generated face images. To compare our method with traditional CycleGAN and recent algorithms such as XGAN and CariGAN, both subjective and objective evaluations were conducted. Subjectively, our method demonstrates more natural color transitions and superior artifact elimination, achieving higher scores in Mean Opinion Score (MOS) evaluations. Objectively, experiments using our method yielded better scores across three metrics: FID, SSIM, and MS-SSIM. The effectiveness of the proposed methods is validated through both objective and subjective evaluations.  
generative adversarial network; facial style transfer; AdaLIN; Laplacian regular +
Footnote †: journal: Applied Sciences
# Buffer Allocation via Bottleneck-Based Variable Neighborhood Search
## 5 Conclusions and Future Work  
This study presented an efficient buffer allocation approach based on a bottleneck indicator and a variable neighborhood search algorithm to solve the buffer allocation problem in production lines. The active probability index as the bottleneck indicator was proposed to predict the most promising buffer updating locations. Based on the active probability index, the real bottleneck of the production line can be detected with a small number of searches, thereby decreasing computation time.  
The variable neighborhood search algorithm was used to improve solution quality. If no satisfactory solution was generated for the bottleneck in the initial neighborhood structure, an additional neighborhood structure was used to search for a better solution. To prove the effectiveness of the proposed buffer allocation approach, it was compared with four other buffer allocation methods. The numerical examples showed that the proposed approach achieves a good balance between solution quality and computation time. In the large-scale experiment, the proposed approach achieves the highest solution quality and allocates buffers more rapidly than the other four methods. It requires less than 8 h even for the buffer allocation of the production line with 50 nodes.  
In summary, the study contributes toward efficient buffer allocation for the rapid estimation of topology and the potential value of a production line. In particular, in the phase of feasibility analysis, the proposed approach can be used to rapidly estimate the handling capability and cost of a production line.  
The proposed buffer allocation approach can only solve the efficient buffer allocation problem for a production line with one kind of job. In the future, the proposed approach should be improved to allocate buffers efficiently for a production line with multiple kinds of jobs.  
Conceptualization, S.G., T.H., T.K., K.T., J.I.U.R., and J.O.; methodology, S.G.; software, S.G.; validation, S.G., T.H., T.K., K.T. and J.O.; formal analysis, S.G.; investigation, S.G.; writing--original draft preparation, S.G.; writing--review and editing, S.G. and O.J.; supervision, O.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 2 Instrument Design
### Critical Components  
#### 2.2.1 Feed Cables  
The Pickett horn antenna features two ports, one for horizontal polarization (H) and one for vertical (V). As shown in Figure 2, the noise signals \(T_{\mathrm{B}}\)\({}^{p}\) (\(p\) = H, V) received are connected to the radiometer input ports, which can be switched _via_ the input switch. The feed cables (FC) used are 0.3 m Huber+Suhner SUCOFLEX 106 coaxial cables with N-connectors on the antenna-side and SMA-connectors on the radiometer-side. These cables have losses of 0.26 dB\(\cdot\)m\({}^{\text{-1}}\) at 1.4 GHz. The transmission loss \(L_{\mathrm{FC}}\)\(\approx\) 0.1 dB (corresponding to the power transmission factor \(t_{\mathrm{FC}}\)\(\approx\) 0.98) of the FC is measured at ambient temperature and for the center frequency 1,413.5 MHz of the radiometer.  
In analogy with (4) and (5), the noise temperatures at the radiometer inputs are \(T_{\mathrm{RM}\,\mathrm{in}}\)\({}^{p}\) = \(T_{\mathrm{B}}\)\({}^{p}\)\(\cdot\)\(t_{\mathrm{FC}}\) + \(T_{\mathrm{FC}}\)-(1 - \(t_{\mathrm{FC}}\)), where \(T_{\mathrm{FC}}\) is the temperature of the FC. The noise \(\Delta T_{\mathrm{B,\,FC}}\) = \(T_{\mathrm{RM,in}}\)\({}^{p}\) - \(T_{\mathrm{B}}\)\({}^{p}\) added by the FC is:  
\[\Delta T_{\mathrm{B,\,FC}}=\left(1-t_{\mathrm{FC}}\right)\left(T_{\mathrm{FC }}-T_{\mathrm{B}}^{\,p}\right) \tag{6}\]  
This shows that the impact of the FC does not only depend on its loss, but also on the difference between \(T_{\mathrm{B}}\)\({}^{p}\) and \(T_{\mathrm{FC}}\). In ground-based applications, the air temperature is a reasonable approximation for \(T_{\mathrm{FC}}\), whereas \(T_{\mathrm{B}}\)\({}^{p}\) is generally smaller. In these cases, \(\Delta T_{\mathrm{B,FC}}\) is positive, implying that the thermal noise of the FC dominates its absorption. According to (6), \(\Delta T_{\mathrm{B,FC}}\) is largest for low \(T_{\mathrm{B}}\)\({}^{p}\) as measured with the instrument oriented towards the sky, and \(\Delta T_{\mathrm{B,\,FC}}\) decreases with increasing \(T_{\mathrm{B}}\)\({}^{p}\) (e.g., for \(T_{\mathrm{FC}}\) = 300 K and \(T_{\mathrm{B}}\)\({}^{p}\) = \(T_{\mathrm{sky}}\)\(\approx\) 5 K \(\Rightarrow\)\(\Delta T_{\mathrm{B,FC}}\) = 6.7 K, and for \(T_{\mathrm{B}}\)\({}^{p}\) = 150 K \(\Rightarrow\)\(\Delta T_{\mathrm{B,FC}}\) = 3.4 K).  
In Section 3, this simple model will be used for correcting the contribution \(\Delta T_{\rm B,FC}\) of the FC on the measurements \(T_{\rm RM,in}\). However, the model is not perfect because \(T_{\rm FC}\), for example, is not constant along the FC. Therefore, \(\Delta T_{\rm B,FC}\) cannot be perfectly modeled, which makes it especially important to reduce the losses of the FC as far as possible.
## 2 Materials and Methods
### Numerical Model
#### 2.2.2 Model Validation  
The bottom roughness of the tidal model is determined by adding the basic roughness and the water depth variation roughness, with a value of 0.015 + 0.01/h [27]. The horizontal  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Serial Number** & **Satellite** & **Sensor** & **Spatial Resolution (m)** & **Track Number** & **Imaging Date** \\ \hline
1 & Landsat-5 & TM & 30 & 034-121 & 2000-01-03 \\
2 & Landsat-5 & TM & 30 & 033-122 & 2000-01-10 \\
3 & Landsat-5 & TM & 30 & 033-122 & 2000-02-11 \\
4 & Landsat-5 & TM & 30 & 033-122 & 2000-03-30 \\
5 & Landsat-5 & TM & 30 & 034-122 & 2000-09-08 \\
6 & Landsat-5 & TM & 30 & 034-121 & 2000-09-15 \\
7 & GaoFen-6 & PMS & 2 & 69-599 & 2020-01-10 \\
8 & GaoFen-6 & PMS & 2 & 70-599 & 2020-01-10 \\
9 & GaoFen-6 & PMS & 2 & 69-601 & 2020-02-08 \\
10 & GaoFen-6 & PMS & 2 & 70-601 & 2020-02-08 \\
11 & GaoFen-6 & PMS & 2 & 69-599 & 2020-03-28 \\
12 & GaoFen-6 & PMS & 2 & 70-599 & 2020-03-28 \\
13 & GaoFen-6 & PMS & 2 & 69-602 & 2020-04-18 \\
14 & GaoFen-6 & PMS & 2 & 69-600 & 2020-09-04 \\
15 & GaoFen-6 & PMS & 2 & 70-600 & 2020-09-04 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Remote sensing image data information.  
eddy viscosity is calculated using the Smagorinsky formula [28], and the Smagorinsky coefficient Cs is 0.28. The critical Courant number (CFL) is 0.8. The time step is controlled within a range of 0.01 to 120 s. The Coriolis force is calculated based on the geostrophic angular velocity and geographic latitude of the grid cell.  
The model selects the tidal level of 26 stations and the tidal current observation data of 8 stations for verification (Figure 1), including 26 tide observation stations and 8 tidal current observation stations. The tide verification lasted for half a month and 25 h, and the tidal current verification was carried out during the high-tide period. The verification of this paper is a continuation of existing research results [3; 29]. The verification results of each station show that the simulated tidal level and flow velocity processes are basically consistent with the field observations, and the maximum error of the tidal level does not exceed 10% of the maximum tidal range; the average absolute error of the tidal flow velocity does not exceed 20% of the tidal amplitude, and the average absolute error of the flow direction does not exceed 15\({}^{\circ}\). The model verification accuracy meets the requirements of the Technical Guidelines for Environmental Impact Assessment of Bay Reclamation Planning (GB/T 29726) [30]. This shows that the parameters used in the model are reasonable, which can better simulate the tidal movement process in the study area and provide a reliable dynamic field for the calculation of tidal range and tidal prism.
## 4 Conclusions  
WPC with high mechanical properties and hydrophobicity was successfully prepared by one-step hot moulding using different plastic wastes and wood flour. The properties of WPC prepared by different plastics combined with different proportion were compared, which was also the innovation of this research. The WPC produced with wood flour to ABS weight ratio of 80:20 showed the best mechanical strength and hydrophobicity. On the one hand, the excellent properties of WPC were related to the collapse of the cell wall during the hot-moulding process combined with the good physical strength and desirable chemical bonding presence between the fibers. On the other hand, the viscous state of plastic covered the surface of the fiber or occupied part of the cracks, which further reduced the amount of hygroscopic hydroxyl groups exposed and hence improved the hydrophobicity of the material. In addition, more pressing regimes can be researched to reduce the preparation cost of materials. Overall, the proposed hot moulding approach represents an effective method to recycle plastic wastes by transforming them into promising WPC that could replace the traditional fiberboard for use in furniture production.  
The following are available online at [[https://www.mdpi.com/article/10.3390/app11156838/s1](https://www.mdpi.com/article/10.3390/app11156838/s1)]([https://www.mdpi.com/article/10.3390/app11156838/s1](https://www.mdpi.com/article/10.3390/app11156838/s1)).  
Conceptualization, Q.Y.; Data curation, H.Y.; Formal analysis, Y.S. ([PERSON]) and R.Y.; Funding acquisition, Q.Y.; Investigation, Y.S. ([PERSON]); Methodology, Y.W.; Resources, Y.W. and W.F.; Software, H.Y. and C.X.; Writing--original draft, Q.Y.; Writing--review & editing, M.Z. and S.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the China Postdoctoral Science Foundation (2021M690847), Natural Science Foundation of Jiangsu Province (BK20200775), \"Nan Taihu Lake Elite Plan\" Project ([2018] No.2, Huzhou, Zhejiang Province), Scientific Research Project of Hunan Provincial Department of Education (20C1903) and Advanced Analysis and Test Center of Nanjing Forestry University.  
Not applicable.  
The data presented in this study are available on request.  
The authors thank the China Postdoctoral Science Foundation (2021M690847), Natural Science Foundation of Jiangsu Province (BK20200775), \"Nan Taihu Lake Elite Plan\" Project ([2018] No.2, Huzhou, Zhejiang Province), Scientific Research Project of Hunan Provincial Department of Education (20C1903) and Advanced Analysis and Test Center of Nanjing Forestry University.  
The authors declare no conflict of interest.  
Figure 8: (**a**) ATR FT-IR spectra of WPS and poplar. (**b**) XRD patterns of WPS and poplar.