esa-sceva/satcom-chunk-collection huggingface dataset:
# Data and Knowledge-Driven Bridge Digital Twin Modeling for Smart Operation and Maintenance
## 4 Spatiotemporal Data Collection and Processing for Bridge Digital Twin  
This chapter systematically presents the key methods for data and expert knowledge collection and processing that are critical to the development of DTM of bridges. Accurate data collection and efficient processing are paramount for real-time monitoring and the early detection of structural deterioration. First, the chapter introduces techniques for 2D image data collection and the application of the YOLOv8 model to automatically detect surface defects, such as cracks and spalling. Next, the chapter discusses the application of LiDAR and related technologies to acquire 3D point cloud data, with the application of PointNet algorithms to reconstruct the three-dimensional geometric model of the bridge, which is fundamental for spatial analysis. Then, the chapter presents the collection and processing of sensor time series data, emphasizing the monitoring of key physical parameters, including stress, strain, and environmental conditions, to enable real-time structural assessment. Finally, this chapter presents the process of collecting and processing expert knowledge, establishing an expert knowledge base through fuzzy logic. The data processing procedure of the DTM is shown in Figure 2.  
Figure 2: The data processing procedure of the digital twin model.
# A Method Improves Speech Recognition with Contrastive Learning in Low-Resource Languages
## 4 Experimental Methods  
### Corpus  
In this paper, we used speech datasets of four languages to complete our experiments, namely, Mandarin (MA), English (EN), Uyghur (Uy), and Uzbek (Uz). The dataset details are shown in the following Table 1.  
The English speech dataset used in this study is the Librispeech dataset [36], which is an audiobook dataset containing both text and speech data. It consists of approximately 1000 h of 16 kHz speech data, which are segmented, trimmed, aligned, and organized into audio files of approximately 10 s duration along with their corresponding text annotations. For pre-training, we use the train-clean-100 subset of the dataset, which includes around 100 h of speech data and corresponding text annotations, out of which 20 h are randomly selected for fine-tuning. The test-clean subset, consisting of 5.4 h of speech data and corresponding text annotations, is used as the test dataset.  
The Mandarin speech datasets used were AISHELL-1 [37] and THCHS-30 [38]. The AISHELL-1-train subset was used as the model pre-trained unlabeled speech data, which contains approximately 150 h of 16 kHz speech data, 340 speakers, and 120,098 utterances. The THCHS-30 train was used as the fine-tuning dataset, and THCHS-30-test was used as the test dataset.  
For Uyghur, we used the validated part of Common Voice Corpus 11.0-Uyghur [39], a subset containing about 107 h of speech and corresponding text data, of which 20 h were randomly selected as a fine-tuning dataset and 5 h as a test dataset.  
The validated part of Common Voice Corpus 11.0-Uzbek [39] was used for Uzbek. This subset contains about 97 h of speech and corresponding text data, of which 20 h were randomly selected as a fine-tuning dataset and 5 h as a test dataset.
# Anomaly Detection for IOT Systems Using Active Learning
## 4 Proposed Methodology
### Uncertainty Sampling
The formula for entropy-based uncertainty sampling is as follows:  
\[x\_i=argmax\_x\ \sum\text{C\_j}=1\ (-p\_ij\ log(p\_ij)) \tag{5}\]  
where p_ij is the anticipated probability of class j for data point x_i, C is the number of classes, and x_i is the data point with the maximum entropy. Another statistic to consider is the margin, representing the difference between the two highest projected probabilities. Margin-based uncertainty sampling is employed to select data points that exhibit the smallest discrepancy between the two highest projected probabilities. The formula for margin-based uncertainty sampling is as follows:  
\[x\_i=argmax\_x\ min\_j\in\{1,\dots,C\},j\
eq y\_i\ (p\_ij-p\_ij\_i) \tag{6}\]  
where x_i is the data point with the lowest margin, C is the number of classes, y_i is the accurate class label for data point x_i, and p_ij is the estimated likelihood that class j will occur for data point x_i. Variance, which refers to the degree of fluctuation in the model's predictions, constitutes a third metric. Variance-based uncertainty sampling is employed to select the data points that exhibit the most significant variability in the model's predictions. The mathematical expression for uncertainty sampling, which is determined by variance, can be represented by the following formula:  
\[x\_i=argmax\_x\ (1/T)\ \sum\text{T\_t}=1\ (p\_it-(1/T)\ \sum\text{T\_t}^{ \prime}=1\ p\_it^{\prime})2 \tag{7}\]  
where T is the total number of model predictions, p_it is the projected probability of class j for data point x_i in the t-th model prediction, and x_i is the data point with the highest variance. Active learning can attain superior model performance using a reduced number of labeled data points by selecting the most valuable data points for labeling. This approach is particularly advantageous when obtaining labeled data is challenging or costly. The active learning process involves training a model iteratively on a limited portion of the available data, referred to as the active learning set. This trained model is then utilized to select the next set of data points that require labeling, which is subsequently added to the active learning set. The procedure above is commonly referred to as active learning. The process above continues iteratively until the desired level of precision is achieved, or a predetermined stopping criterion is met. To reduce the amount of labeled data required for training and to enhance the model's accuracy, active learning endeavors to select the most informative and representative data points for annotation.
## 1 Introduction
Their study involved a 10 min warm-up protocol consisting of submaximal cycling followed by dynamic stretching and exercises involving vertical jumps after PAPE. [PERSON] et al. [14] conducted a study to determine how PAPE affects explosive power performance in the lower extremities. Their warm-up protocol before PAPE included 10 min of cycling at a constant power (1 W per kg of body mass) and 5 min of dynamic stretching exercises (involving hips, knees, and ankles, as well as squats, jumps, and sprint movements) for a total of 15 min. However, none of these studies have specifically focused on whether the stretching techniques used in the warm-up prior to PAPE affect post-PAPE athletic performance. Although warm-up exercises consisting of dynamic exercises following cycling are often preferred as pre-PAPE [15] warm-up protocols in the literature, there is a lack of standardization in terms of application methods and duration. Furthermore, how PAPE may be influenced by other stretching techniques used in warm-up is a subject of curiosity. In this context, a crucial question that remains to be addressed is what optimal warm-up exercises should be implemented before PAPE to maximize its effects. Optimizing athlete performance through PAPE can provide significant advantages in competitions. Therefore, determining the optimal stretching technique applied during warm-up exercises before PAPE becomes crucial to induce the desired PAPE effects. In this context, the aim of this study was to examine the effects of different stretching techniques used during warm-up exercises preceding PAPE on athletes' explosive lower extremity strength performance. Considering the results of existing studies in the literature, the hypothesis of this research is that the most effective stretching technique applied during the warm-up phase is the dynamic stretching technique.
# Noise Analysis and Structural Optimization of Automobile Scroll Compressor Air Valve
## 5 Verification of Sound Field Simulation Results
### Comparison of Surface Sound Power Levels  
Surface sound power refers to the energy distribution of sonic energy generated by surface fluid flow, which is fundamentally related to eddy currents, turbulence, and other fluid movements. This metric serves as a critical indicator for assessing the noise pollution from fluid flow.  
The comparative analysis in Figures 15 and 16 shows the internal flow field simulations for the unmodified end cap and the one with a split chamber. A significant reduction of 17 dB in the maximum surface acoustic power level is observed for the chambered end cap, indicating its effectiveness in noise reduction. This improvement is attributed to the design of the chamber, which facilitates wave reflection, absorption, and energy dissipation, resulting in a more uniform distribution of acoustic power. When the valve opens, sound waves enter the chamber through the exhaust port, and upon hitting the wall, they are reflected. These reflections create interference with the incoming sound wave. If the phase difference between the two is 180\({}^{\circ}\), they cancel each other, contributing to noise reduction. The chamber division serves two purposes: it reduces the likelihood of sound waves reaching the exhaust directly, causing more waves to interact with the walls and dissipate energy (absorption). In addition, the longer path within the divided chambers further dissipates sound energy.
# Bayesian Compressive Sensing Based Optimized Node Selection Scheme in Underwater Sensor Networks
## 5 Conclusions  
In this work, an optimized sensor node selection scheme for compressed information acquisition is provided for improving the accuracy of reconstruction and prolonging the network lifetime. The signal reconstruction is achieved based on Bayesian estimation theory. The MSE of estimation can be obtained by the given error covariance matrix. Then, the proposed optimization problem aims at minimizing it by taking the selection matrix as the variable. By relaxing the non-convex constraint, the proposed optimization problem can be solved effectively. To increase the sustainability of the network, the scheme selects the nodes with more energy by adding a bound constraint in the  
Figure 4: Remaining energy of each sensor node.  
Figure 5: Energy efficiency versus different number of selected sensor nodes.  
optimization problem. Compared with the conventional node selection scheme, simulation results demonstrate that the proposed scheme can achieve more accurate reconstruction and improve the network lifetime significantly.  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the experiments, [PERSON] and [PERSON] analyzed the data, [PERSON] and [PERSON] performed the experiments, [PERSON] wrote the paper, [PERSON] enhanced the paper.  
**Funding:** This work is supported by National Natural Science Foundation of China (Nos. 61501139, 61371100, 61401118).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# MSPNet: Multi-Scale Strip Pooling Network for Road Extraction from Remote Sensing Images
## 2 Related Work
### Expert Knowledge-Based Methods  
Conventional expert knowledge-based algorithms for road extraction usually utilize geometric and physical features to match. [PERSON] et al. [12] propose a road detection method based on a Circular Projection (CP) matching and tracking strategy, which is beneficial for twisty roads detection. [PERSON] et al. [5] present a morphological method by combining the automatic thresholding and morphological operation techniques to extract roads from remote sensing images. [PERSON] et al. [13] propose an automatic road extraction method for vague aerial images with an improved Canny edge detection operator and Hough line transform algorithm. [PERSON] et al. [14] propose a road extraction based on zebra crossings detection. [PERSON] et al. [15] develop an approach for road extraction utilizing pixel spectral information for classification and image segmentation-derived object features. However, these methods based on hand-designed road features are generally inefficient and unsuitable for large-scale remote sensing data.
## 2 Fifth-Generation Network Slicing and Related Work
### Network Slice Service Planning  
The most frequently cited use case for network slicing is presented in [10] and is based on three 5G scenarios: eMBB for high data rates, high user density, and high user mobility; mMTC for a small number of latency-insensitive data; and URLLC for strict requirements for throughput, latency, and reliability. For example, in a self-driving car, a user could simultaneously access different network slices. A self-driving car is connected to the network through vehicle-to-everything (V2X) communication. The user sitting in the car initiates a service for HD video streaming using the system available in the car. V2X communication requires low latency but not necessarily a high data rate, while HD video streaming requires a high data rate but is latency-tolerant. Thus, the V2X communication service and HD video streaming service connect to different network slices, such as a URLLC slice and an eMBB slice.  
Network slices could be organized for mobile virtual network operators (MVNOs). This is the use case where a mobile network operator (MNO), as a network slice provider, leases its radio resources to MVNOs as network slice service providers who do not own wireless network infrastructure. MNO and MVNOs conclude SLAs that define the boundaries of slices depending on the requirements of services. Another often used case is organizing network slices for different traffic types, for example, for guaranteed bit rate (GBR) traffic with a minimum-bit-rate guarantee and for non-GBR traffic without such guarantees. Traffic could be viewed as streaming traffic when a service generated it and is characterized by its duration, while elastic traffic services are characterized by the size of the transmitted data.  
Table 1 provides references with the above-mentioned examples of network slice service planning mechanisms.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Mechanism** & **Ref.** \\ \hline \multicolumn{2}{c}{Network slice service planning mechanisms} \\ \hline eMBB, mMTC, and URLLC & [4; 11; 12] \\ Mobile virtual network operators (MVNOs) & [13] \\ Traffic type: guaranteed bit rate (GBR), non-GBR, streaming, elastic & [14] \\ \hline \multicolumn{2}{c}{Resource capacity planning mechanisms} \\ \hline Weights for services according to bit-rate or latency requirements & [12; 15; 16; 17; 18; 19; 20; 21] \\ Service-level agreement (SLA) & [22] \\ Slice priorities & [18; 23; 24; 25] \\ Auction model & [26; 27; 28] \\ Isolation to guarantee the minimum and/or maximum bit rate & [16; 18; 29; 30; 31; 32; 33] \\ QoS degradation & [34] \\ Three equally weighed criteria: (i) ratio of the average throughput to average delay of one user/slice; (ii) average network throughput subject to average delay constraints; (iii) barrier function \\ Three equally weighed criteria: (i) co-occurrence rate; (ii) maximum deviation; (iii) the time interval within which the deviation is possible \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of selected works on network slicing mechanisms.
# Ozone Activation on TiO\({}_{2}\) Studied by IR Spectroscopy and Quantum Chemistry
## 2 Experiment  
### FTIR Spectroscopy  
Batches of TiO\({}_{2}\) powder (Hombifine N 340 m\({}^{2}\)/g) were pressed into tablets and pretreated by heating at 723 K in vacuum for 1 h and then cooled in oxygen to avoid the reduction.  
The experimental setup and the procedure of ozone adsorption have previously been described in detail [6; 7; 8]. The low-temperature stainless steel cell for studying the infrared spectra of adsorbed species was described elsewhere [11]. Ozone was prepared from gaseous \({}^{16}\)O\({}_{2}\) or its mixtures with \({}^{18}\)O\({}_{2}\) in the electric discharge and manipulated as previously reported. Before recording the spectra, about 0.5 Torr of helium was introduced into the cell to improve thermal contact of the sample with the cold environment. Spectra were recorded with a Nicolet FTIR 710 spectrometer with 2 cm\({}^{-1}\) resolution. To eliminate the bulk absorption, the spectra of cooled samples were subtracted from those after exposure to ozone. For the deconvolution of the complex bands of isotopically mixed ozone the MagicPlot 2.9 program was used. The width was set at 6.6 cm\({}^{-1}\), and band positions were fixed.
# Research on the Performance of an Aircraft Landing
## 6 Conclusions  
(1) In this paper, the combination of a numerical continuation algorithm and bifurcation theory was applied to the design and analysis of a hook lock mechanism for aircraft landing gear. The mechanism underlying the unstable jumping phenomenon of the lock mechanism was elucidated. Compared to conventional dynamical methods, the proposed combined method has unique advantages concerning the stability analysis of the mechanism, making it especially effective for use in the early stage of aircraft R&D.  
(2) The hook lock mechanism is prone to pseudo-lock-up when the influence of the landing gear weight is taken into consideration. This paper analyzed in detail the conditions under which the lock mechanism can unlock automatically at various landing gear overloads. Additionally, the paper also derived the critical angle beyond which the hook lock can be in a completely locked state, thereby providing a design basis for the mounting position of the limit stopper for the lock mechanism.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Configuration** & \(k\) **(N/mm)** & \(lu\) **(mm)** & \(c_{2}\) **(mm)** & \(\gamma_{2}\) **(degree)** & \(\theta_{1}^{c}\) **(degree)** & \(F_{Unlock}\) **(N)** \\ \hline Default & 1.0 & 60.0 & 21.6 & 42.2 & 138.5 & 78.0 \\ Optimization & 0.5 & 39.5 & 28.4 & 46.2 & 138.0 & 68.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Optimization results for minimizing the unlocking force.  
Figure 13: The optimization process based on ISIGHT combined with MATLAB.  
(3) The influences of the lock spring parameters and mounting position on the performance of the lock mechanism were analyzed, and the unlocking force of the lock mechanism was optimized based on MIGA. The optimization results demonstrate that the unlocking force is reduced by 12.2% while ensuring the locking ability of the lock mechanism.  
Conceptualization, H.N. and X.W.; Methodology, Y.Y., X.W. and H.N.; Data curation, K.X. and Z.Z.; Software, K.X.; Validation, Y.Y.; Formal analysis, K.X.; Investigation, Y.Y. and K.X.; Resources, H.N. and X.W.; Writing--original draft preparation, K.X.; Writing--review and editing, Y.Y.; Supervision, H.N. and X.W.  
This work was financially supported by the National Natural Science Foundation of China (51805249), the Natural Science Foundation of Jiangsu Province (BK20180436) and the Priority Academic Program Development of Jiangsu Higher Education Institutes.  
The authors declare no conflict of interest.
# A Type of Low-Latency Data Gathering Method with Multi-Sink for Sensor Networks
## 2 Related Works
### Random Mobility  
For the random mobility scheme, the mobile Sinks are often mounted on people or animals moving randomly to collect the required information [13]. However, due to its random mobility, it is difficult to bind the data transfer latency with the data delivery ratio [13]. Data is stored at the node temporarily to be transmitted when the mobile Sink is at the most favorable location in order to achieve the maximum network lifetime.  
[PERSON] _et al._[32] proposed a type of data gathering method with the help of one mobile Sink. Due to the randomness of trajectory, the Sink could communicate with each node with the same probability. However, this method may increase the load of hot spots and the random movement of Sink will cause frequent breakage as well as reconstruction of communication link for the network. Moreover, it will also increase extra energy consumption and time delay.  
A Virtual Circle Combined Straight Routing (VCCSR) protocol for data collection with a mobile Sink is proposed in [33]. VCCSR selects a set of cluster heads located near the virtual backbone. When the Sink issues a query in the network, a spanning tree is constructed to collect and complete data periodically. The goal of the proposed algorithm is to decrease the reconstruction cost and increase the data delivery ratio. However, a large number of nodes participate in routing, increasing network overhead.  
In addition, [PERSON] _et al._ proposed an elastic routing algorithm to minimize the traffic and enhance the reliability of data uploading from nodes to the mobile Sink [34]. When a Sink moves, the new location information is propagated along the reverse geographic routing path to the source during data delivery. However, this hop-by-hop data transmission could also increase the latency in data collection.
# Evaluation of 8-Channel Radiative Antenna Arrays for Human Head Imaging at 10.5 Tesla
## 3 Results
### Comparison of the 8-Channel Monopole and Sleeve Antenna Arrays  
As shown in Figure 4, we compared the 8-channel monopole and sleeve antenna arrays to evaluate the end-fed antenna arrays. Figure 4 shows the simulated (Figure 4a,b)) and experimental (Figure 4c,d) B1\({}^{+}\) efficiency comparison between the monopole and sleeve antenna arrays in the central axial and the coronal planes. Within the ROI in Figure 4, the 8-channel monopole antenna array displayed 24.4% and 30.4% higher B1\({}^{+}\) efficiency than the 8-channel sleeve antenna array in the simulation and the experiment, respectively.  
The expected peak 10 g SAR values within the phantom of the 8-channel monopole (Figure 4e) and sleeve (Figure 4f) antenna arrays are 0.47 W/kg and 0.28 W/kg, respectively. Compared to the 8-channel monopole antenna array, the results indicate 40.4% higher peak 10 g SAR values of the 8-channel sleeve antenna array. Due to the lower peak 10 g SAR values of the 8-channel sleeve antenna array, both the monopole (Figure 4g) and the sleeve (Figure 4h) antenna arrays showed similar SAR efficiency values.  
Comparison of the Dipole, Monopole, and Sleeve Antenna Arrays with Human Model and Human Cadaver Images with the Sleeve Antenna Array  
When the simulated human model data were compared within the ROI in Figure 5a-c), the 8-channel sleeve antenna array displayed similar B1\({}^{+}\) efficiency to the 8-channel dipole antenna array, and 22% lower B1\({}^{+}\) efficiency than the 8-channel monopole antenna array. However, due to the lower peak 10 g SAR values, the 8-channel sleeve antenna array (Figure 5f) showed 23.8% higher and 21% higher SAR efficiency compared to the 8-channel dipole antenna (Figure 5d) and monopole antenna (Figure 5e) arrays, respectively.  
The sleeve antenna array showed relatively small coverage in the phantom (Figure 4b,d). However, with the human model, the sleeve antenna (Figure 5c) did not show smaller B1+ coverage compared to the dipole (Figure 5a) nor the monopole (Figure 5b) antenna arrays.  
Figure 6 shows coronal high-resolution TSE and GRE cadaver images obtained at 10.5 T with the 8-channel sleeve antenna array, which demonstrate overall field distribution and  
Figure 4: Simulated (**a,b**) and experimental (**c,d**) B1+ efficiency maps of the 8-channel monopole and sleeve antenna arrays in the axial and coronal planes. Simulated 10 g SAR (**e,f**) and SAR efficiency (**g,h**) maps of the 8-channel monopole and sleeve antenna arrays in the axial and coronal planes.  
Figure 5: Simulated B1+ efficiency (**a–c**) and SAR efficiency (**d–f**) maps of the 8-channel dipole, monopole, and sleeve antenna arrays with coaxial cables in the axial, coronal, and sagittal planes.  
coverage. Uniform whole-brain images were achieved with the 8-channel sleeve antenna array utilizing B1 phase shimming technique, while maintaining equal RF amplitude per channel. Optimized pTx excitation techniques can further improve homogeneity and local B1+ efficiency.