esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study explores a novel flow control approach for enhancing the energy harvesting performance of an oscillating foil, which is a promising technology for harvesting renewable energy from wind and ocean currents. This approach involves placing a cylindrical spoiler structure at the trailing edge of the foil and investigating the effects of its diameter and spacing on the energy harvesting efficiency. The results show that the spoiler cylinders can significantly increase the energy harvesting efficiency of the foil by up to 19.26% compared to the case without them. The optimal configuration for the spoiler cylinders is found to be 11.0% of c in diameter, 0.5% of c in transverse spacing, and 0 in longitudinal spacing. Moreover, this flow control method is proven to be highly effective under a wide range of motion kinetic parameters. The findings of this study provide technical guidance for the design and implementation of oscillating foil energy harvesting devices, highlighting their practical engineering value.  
osclating foil; energy harvesting; trailing edge; cylindrical spoiler structure +
Footnote †: journal: Applied sciences
## 5 Conclusions  
This paper mainly focuses on the modeling of DPS in LEO-SIN. In our studies, we found some interesting points. First, the DPS in LEO-SIN suffers from some unique characteristics such as the main path not being at the center point, asymmetrical, and random multi-path with random AOA, which are quite different from the GEO satellite network and TMCN. Second, the traditional SOS model cannot describe and simulate the DPS accurately enough. We proposed a novel RMEDS-SP to solve the above problems to achieve the advantage of MEDS-SP and RMEDS. Third, all the SOS models would have some accumulated modeling errors at a relative time. The periodic features can be exploited by the periodicity of a satellite moving to improve the DPS model. So, we exploit the piecewise functions to the ACF of DPS to converge the modeling error supposition with time by periodic changes. Fourth, the simulation models are aimed towards applications. We further implemented this model to the hardware and construct the test environment to analyze the performance.  
Above all, the simulations and analyses have shown that the proposed methods have better performance of modeling accuracy than other models, which is clearly shown in Table 4. Compared with other models, our new model can have a better error control and resource utilization, but its design difficulty and complexity inevitably increased. Since we use the periodic motion characteristics of satellites to conduct error convergence for ACF of DPS, our model had better adaptability for satellite scenarios and could tolerate larger maximum Doppler extension. In general, our new model would be well applied to low orbit satellite Internet.  
Figure 17: Signal power spectrum realized by MATLAB simulation and hardware.  
Conceptualization, W.L., Y.D., K.W., and Z.D.; methodology, W.L., Y.D., K.W., and Z.D.; software, Y.D.; validation, Y.D., W.L., K.W., and Z.D.; formal analysis, Y.D., Y.L., M.L., and X.Y.; investigation, Y.D.; resources, Y.D., W.L., S.Z., and M.L.; data curation, Y.D.; writing--original draft preparation, Y.D.; writing--review and editing, Y.D., W.L., and H.L.; visualization, Y.D.; supervision, W.L., K.W., and Z.D.; project administration, W.L., Y.D., K.W., and Z.D.; funding acquisition, W.L., K.W., and Z.D. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (61801039), and China National Key Research and Development Plan (2020 YFB1808005 and 2019 YFB1803105), and the Civil Aerospace Technology Advance Research Project, and the Fundamental Research Funds for the Central Universities (2021 RC06).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Data-Augmented Hybrid Named Entity Recognition for Disaster Management by Transfer Learning
## 3 Methodology
### Augmented Model  
The previous module, the reference model, achieves the preliminary recognition of Mandarin sentences and words. However, to recognize messages pertain to disaster management, this research would have to build a comprehensive and enormous training set to train the reference model if transfer learning is not adopted. Unfortunately, as mentioned in Section 1.3, the lack of adequate corpora regarding disaster management hinders the development of a NER model from scratch. Therefore, this research utilizes transfer learning to address the difficulty of constructing a specialized NER model. The augmented model is constructed based on the reference model. With the introduction of transfer learning to the existing Bi-LSTM-CRF, this module merges the resources from the data augmentation module by updating the reference model's parameters. One of the solutions to transfer between source tasks and target tasks is label remapping whenever feasible. The mechanism underlying label remapping involves defining a new label set for a target task and mapping the original label set from the source task to the new one individually. However, no one-to-one relationship exists between source tags and target tags in this research. Hence, the augmented model realizes the process through a cross-domain transfer with disparate label sets [17].  
Figure 5 illustrated the structure of the augmented model. Referring to the flowchart, this module first initializes the source and target models whose main structures inherited from the reference model which is labeled as 1. The two submodels share the same augmented training data from the data augmentation module but with disparate label sets, hence the initialization of parameters are separate labeled as 2. Subsequently, the training process enters to several times of epoch iterations which is labeled as 3. Each epoch iteration parses the whole training data into different sub-training sets to execute a batch iteration. In each batch iteration, which is labeled as 4, the two submodels in the augmented model are trained through Bi-LSTM together, but they undergo distinct CRF processes separately concerning the corresponding label sets for their tasks. Meanwhile, in the process of model optimization, the two submodels have the same shared parameters but with different specific parameters. The former is jointly optimized for the two submodels and updated after each batch iteration. The latter is extended from the former and is optimized and updated, depending on their own label sets. After completing all the batch iterations, an epoch iteration accomplished. The augmented model is constructed after several epoch iterations and can finally perform NER tasks in disaster management.
# Rare Earth Elements Uptake by Synthetic Polymeric and Cellulose-Based Materials: A Review
## 7 Synthetic Polymeric Materials for REE Uptake  
Ion exchange resins have been widely used to capture REEs; however, the performance and selectivity of those commercial resins have limited their usage to preconcentration steps and primary separation in the whole process of REE extraction and recovery [27; 28; 29]. Polymeric adsorbents/ion exchange materials and composites, which include polymeric organic and inorganic hybrid materials, have been developed to achieve better performance and selectivity toward REE recovery (Table 2). These materials incorporate carbonyl and nitrogen groups, showing the maximum capture capacity of REEs at low acidic or neutral pH values.
## 3 Results and Discussion
### Effect of Adsorbent Dosage  
The amount of adsorbent dosage in the solution of heavy metals also affects the adsorption process to great extant because of different surface areas. The effect of the adsorbent dose was determined by adding 0.034, 0.054, 0.054, 0.75, 0.1, 0.13, and 0.16 g of Fe\({}_{3}\)O\({}_{4}\), PANI, and PANI/Fe\({}_{3}\)O\({}_{4}\) composites to 50 mg-L\({}^{-1}\) solutions of Cr(VI) and Ni(II) ions separately. The pH of Cr(VI) was adjusted to 3-4 while that of Ni(II) was adjusted to 7.5 and the solutions were shaken for their corresponding equilibrium time. The results obtained are shown in Figure 8, which shows that removal efficiency of both Cr(VI) and Ni(II) ions depended upon the amount of adsorbent added. Enhancement in the removal of Cr(VI) and Ni(II) ions from the aqueous solution with adsorbent doses was attributed to the larger surface area and the large number of adsorption sites [75]. The results also show that removal efficiency of both Cr(VI) and Ni(II) was same up to 0.1 g of adsorbents but when the amount of adsorbent dose exceeded 0.1 g, the adsorption of Ni(II) became constant while that of Cr(VI) still increased and became constant after 0.14 g of adsorbent. This was due to hydration; Cr(VI) exists as CrO\({}_{4}\)\({}^{-2}\), Cr\({}_{2}\)O\({}_{7}\)\({}^{-2}\), or HCrO\({}^{-4}\) in aqueous solution, which causes an increase in its size or volume. Due to this reason it needs a relatively large surface area and a large number of adsorbent sites as compared to nickel which exist as Ni\({}^{+2}\) ions predominantly. As the concentrations of Cr(VI) and Ni(II) ions are constant, Ni(II) ions finish earlier than Cr(VI) in the aqueous solution [76].  
Figure 8: Effect of adsorbent dosage on adsorption of Cr(VI) and Ni(II).  
Figure 7: Effect of (**a**) contact time and (**b**) temperature on removal of Cr(VI) and Ni(II) ions through Fe\({}_{3}\)O\({}_{4}\), PANI, and PANI/ Fe\({}_{3}\)O\({}_{4}\) composites.
# A Review of Recent Advancements in Knock Detection in Spark Ignition Engines
## 3 Advancements in Knock Detection
### Vibration Signals  
While pressure data are more reliable for measuring knock, almost every vehicle on the road uses a vibration sensor to determine if its engine is knocking ([PERSON] et al., 2018). This is due to accelerometers being less expensive and intrusive than pressure sensors. As shown in Figure 5, the noisy vibration signals are bandpass-filtered, and knock is determined to occur when the oscillations are above a threshold value. While straightforward, this process can be inaccurate, as discussed by [PERSON] et al. (2019), who found that this process was not able to adequately detect knock at high engine speeds.  
Given that most modern knock sensors use vibration sensing, it is a key signal for knock detection. Figure 5 also shows the advances that are being made in vibration sensing technology. While there has been limited work in the advancement of new vibration sensors, an extensive amount of work related to the processing of the signals to achieve higher levels of accuracy has been carried out.
# Part-Based Obstacle Detection Using a Multiple Output Neural Network
###### Abstract  
Detecting the objects surrounding a moving vehicle is essential for autonomous driving and for any kind of advanced driving assistance system; such a system can also be used for analyzing the surrounding traffic as the vehicle moves. The most popular techniques for object detection are based on image processing; in recent years, they have become increasingly focused on artificial intelligence. Systems using monocular vision are increasingly popular for driving assistance, as they do not require complex calibration and setup. The lack of three-dimensional data is compensated for by the efficient and accurate classification of the input image pixels. The detected objects are usually identified as cuboids in the 3D space, or as rectangles in the image space. Recently, instance segmentation techniques have been developed that are able to identify the freeform set of pixels that form an individual object, using complex convolutional neural networks (CNNs). This paper presents an alternative to these instance segmentation networks, combining much simpler semantic segmentation networks with light, geometrical post-processing techniques, to achieve instance segmentation results. The semantic segmentation network produces four semantic labels that identify the quarters of the individual objects: top left, top right, bottom left, and bottom right. These pixels are grouped into connected regions, based on their proximity and their position with respect to the whole object. Each quarter is used to generate a complete object hypothesis, which is then scored according to object pixel fitness. The individual homogeneous regions extracted from the labeled pixels are then assigned to the best-fitted rectangles, leading to complete and freeform identification of the pixels of individual objects. The accuracy is similar to instance segmentation-based methods but with reduced complexity in terms of trainable parameters, which leads to a reduced demand for computational resources.  
driver assistance; CNNs; monocular vision; semantic segmentation; obstacle detection; instance segmentation; vanishing point +
Footnote †: journal: Sensors  
doi: 10.3390/s22124312
## 2 A Brief Description of the Case Study  
The project \"GreenAuto: Green Innovation for the Automotive Industry\" aims to strengthen the automotive industry by promoting the production of low-emission vehicles and optimizing their respective production processes. Within this project, there is a Work Package (WP) dedicated to automated logistics for the automotive industry, which seeks to improve the operational efficiency of the Stellantis manufacturing unit, located in Mangualde, Portugal. To achieve this, solutions for supervision and navigation systems of mobile robots have been developed. Currently, the company lacks a functional fleet management system that allows the monitoring of the location and operational status of mobile robots. This situation is further complicated by the fact that several models and brands are used, making data collection and standardization difficult.  
The type of robots used in the company follows AGV technology, as shown in Figure 1. The AGVs are distributed across the assembly and production lines, encompassing both older models and the latest generation ones. The challenge arises from the differences between the software of the various existing models, as it is necessary to ensure that all AGVs communicate with a server in a standardized way to ensure a smooth operation and efficiency.  
Thus, the approach used aims to develop and implement an efficient and scalable tool to monitor and supervise all mobile robots on the factory floor, ensuring continuous and reliable communication through a mesh network with multiple access points.
# Voice Activity Detection Using Fuzzy Entropy and Support Vector Machine
## 2 Proposed Methodology
### Classifier  
#### 2.4.1 Support Vector Machine (SVM)  
In this work, SVM is used as a classifier, because it constructs an optimal decision function \(f\left(x\right)\), that accurately predicts the unseen data in two classes by minimizing the error function shown in (12),  
\[f\left(x\right)=sign\left(g\left(x\right)\right) \tag{12}\]  
where \(g\left(x\right)\) is the decision boundary derived from the training set samples \(\left(x_{i},\ y_{i}\right)_{i=1}^{N}\), \(x_{i}\ e\ R^{m}\) for the corresponding target classes \(y_{i}\ e\ R^{m}\). The decision boundary is a hyperplane which is given by, as in (13),  
\[g\left(x\right)=\left\langle w,\ x\right\rangle+b \tag{13}\]  
where \(w\) and \(b\), shall be derived based on the classification accuracy of the linear problems. Generally, a nonlinear SVM model is trained to minimize the following objective function in (14),  
\[\begin{array}{l}\min\limits_{w\in\mathcal{D}_{i}}\frac{1}{2}\ \ ||\ w\ ||^{2}+C\sum\limits_{i=1}^{n}\xi_{i}\\ \text{Subject to}\ y_{i}[w^{T}\varphi\left(x_{i}\right)-b]\geq 1-\xi_{i}\\ \xi_{i}\geq 0,\ i=1,2,\ldots,n\end{array} \tag{14}\]  
where \(\varphi\left(x_{i}\right)\) is a mapping function to map \(x_{i}\) to its higher dimensional feature space, \(\xi_{i}\) is the misclassification error and \(C\) controls the tradeoff between the cost of classification and the margin. The mapping of the input training set into a higher dimensional space is done through a kernel function \(K\ \left(x_{i},\ y_{i}\right)\). Usually three types of nonlinear kernel functions are considered, such as polynomial kernel, multilayer kernel, and radial basis function (RBF) kernel. In this work, the RBF kernel function was used because of its excellent generalization and low computational cost [28]. The RBF kernel function is given by (15),  
\[K\ \left(x_{i},\ y_{i}\right)=\exp\left(-\gamma\ ||\ x_{i}-y_{i}\ ||^{2}\right) \tag{15}\]  
Figure 3: Exponential function (\(\exp(-d^{n}/r)\)) for different parameter selection. (**a**) Exponential membership function fixed with \(n=2\), and \(r\) varied from 0.1 to 0.3; (**b**) exponential membership function fixed with \(r=0.2\) and \(n\) varied from 1 to 5.  
where, the parameter \(\gamma=1/2\sigma^{2}\) is the regularization parameter which controls the width of the Gaussian function. For this given kernel function, the error function of the classifier is given by (16),  
\[f\left(x\right)=sign\left(\sum_{i=1}^{N}\alpha_{i}y_{i}\ K\left(x,\ x_{i}\right)+ b\right) \tag{16}\]
## 2 Pump Geometry and Asymmetric Pitch  
### Asymmetric Pitch of Vane-Type Oil Pump  
Figure 1 shows the general form of a vane pump. The vanes are inserted into grooves installed radially in the pump rotor and rotate inwardly in the housing. Applying asymmetric pitches to reduce noise in fluid machines with multiple blades is a generally well-established method [5; 7]. For the vane pump shown in Figure 1, the spacing between each blade can be arranged asymmetrically as in Equation (1).  
The process of arranging asymmetric pitches mainly uses a random number generator. If the positions of the vanes are arranged randomly, there is a lack of design basis for the determined shape, so the effect must be experimentally confirmed through noise and vibration analyses. In addition, since the random number arrangement cannot be artificially adjusted, much trial and error is required to derive the optimal arrangement [6]. In order to solve the above problems an asymmetric pitch generation function is required, which prevents vane drift in a specific part while arranging the vanes with asymmetric pitches and reduces the sound pressure peak, as well as generating the asymmetric pitch periodically to make it easy to predict and control the vane arrangement, as shown in Equation (2) [5].  
\[\Delta\theta_{i}\
eq\Delta\theta_{i-1} \tag{1}\] \[\Delta\theta_{n}=\frac{2\pi}{N}+(-1)^{n}\times A_{m}\bigg{\{}sin \bigg{(}P_{1}\frac{2\pi}{N}n\bigg{)}\times\cos\bigg{(}P_{2}\frac{2\pi}{N}n\bigg{)} \bigg{\}} \tag{2}\]  
where \(\Delta\theta\): incremental angle (pitch) between the teeth; n: order of the teeth; N: total number of teeth; \(A_{m}\): distribution magnitude of asymmetric pitch; and \(P_{1}\), \(P_{2}\): factors exerting an influence on the cycle.
# An Efficient GNSS Coordinate Recognition Algorithm for Epidemic Management
## 6 Simulation Result
### Confirmed Points  
The number of confirmed points \(n_{\text{conf}}\) is discussed in the following. Table 7 provides the values of \(n_{\text{conf}}\). When the radius went from 1/16 to 1/256, \(n_{\text{conf}}\) of c_max went from 142,054 to 4953, \(n_{\text{conf}}\) of c_min went from 107,065 to 2869, and \(n_{\text{conf}}\) of c_avg went from about 121,540 to 3466. With the same radius, the \(n_{\text{conf}}\) of sqr went from 56,237 to 2054, which was less than the values for c_max, c_min, and c_avg. When the radius was 1/16, nconf of c_max was 142,054, and \(n_{\text{conf}}\) of sqr was 56,237. This was a reduction of 85,817 points, and the reduction rate of c_max#sqr was about 60.4% (=85,817/142,054). Figure 13 provides the reduction rates. When the radius was 1/16, the reduction rates of c_max#sqr, c_min#sqr, and c_avg#sqr were about 60.4%, 47.5%, and 53.7%. When the radius was reduced to 1/256, the reduction rates of c_max#sqr, c_min#sqr, and c_avg#sqr were about 58.5%, 28.4%, and 40.7%. Moreover, sqr resulted in a reduction rate of the confirmed points between 28.4% and 60.4%.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Radius** & **c\_max** & **c\_min** & **c\_avg** & **sqr** \\ \hline
1/16 & 90,644 & 59,160 & 73,055 & 34,165 \\
1/32 & 55,710 & 32,970 & 45,370 & 13,113 \\
1/64 & 32,248 & 26,499 & 30,231 & 4512 \\
1/128 & 23,828 & 21,510 & 22,567 & 1995 \\
1/256 & 20,430 & 19,400 & 19,912 & 632 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The numbers of outer points.  
Figure 12: The reduction rate of outer points.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Radius** & **c\_max** & **c\_min** & **c\_avg** & **sqr** \\ \hline
1/16 & 142,054 & 107,065 & 121,540 & 56,237 \\
1/32 & 75,034 & 56,775 & 65,475 & 23,126 \\
1/64 & 30,428 & 24,948 & 28,017 & 16,520 \\
1/128 & 12,478 & 8965 & 11,007 & 5081 \\
1/256 & 4953 & 2869 & 3466 & 2054 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The confirmed points.  
Figure 13: The reduction rates of confirmation points.
## 6 System Performance and Test Case Analysis
### FCW Test Case  
#### 6.2.1 Field Testing Environment  
The TS is deployed in the experimental field of Chongqing Motor Vehicle Mandatory Test Center. The testing center has an internal enclosed road, as shown in Figure 10. It provides C-V2X DUTs from two Tier 1 suppliers, all installed on the vehicles under test.  
Figure 11 depicts the deployment of the test scenario, where a 600 m long straight road is selected for this test, with the RV and the HV positioned 600 m apart from each other. Two node arrays, which could simulate 200 vehicle nodes, are placed 50 m apart.  
Once the hardware equipment is deployed, automated C-V2X testing, including test configuration, monitoring, and test results, can be achieved through an online GUI according to the system's user instructions. Figure 12 displays the administrator login interface of the GUI. Administrator users can view all task executions and completions and access all task reports.  
Figure 11: Test scenario deployment diagram:(**a**) straight road; (**b**) FCW testing scene.  
Figure 10: Field Testing environment and equipment.