esa-sceva/satcom-chunk-collection huggingface dataset:
# Provisioning of Fog Computing over Named-Data Networking in Dynamic Wireless Mesh Systems
## 4 Performance Evaluation
### Numerical Results
#### 4.3.3 Effects of Traffic Type and Caching  
One of the key benefits of NDN systems is the implementation of caching at intermediate nodes. We now examine how caching and different types of traffic impact compute service performance in a static mesh environment. To this effect, Figure 13 illustrates the share of received compute results in time for \(5\times 5\) mesh configuration, with cache enabled (C) and disabled (\(N\)) as a function of the number of consumers and a single producer. Here, we consider two traffic types, CBR and Zipf. It is important to remember that CBR uniformly selects the compute service from all available services, whereas Zipf traffic reflects content popularity, arranging request probabilities according to service popularity as modeled by the Zipf distribution [51].  
Figure 11: Loss and delay performance measures in mobile mesh.  
Figure 12: Share of received compute results in a mobile mesh.  
From the data presented in Figure 13, one may confirm that for both considered traffic types, the share of the received compute results reaches 100% irrespective of whether a cache is enabled or not. Analyzing the presented data further, one may conclude that the effect of cache is non-uniform across other service parameters. In particular, for just a single consumer, the effect of caching is insignificant. However, as the number of consumers increases (and thus the intensity of Interests), caching plays a noticeable role. Notably, the gain between \(K=3\) and \(K=1\) approaches 0.3 for 50 ms, i.e., 0.6 vs. 0.9 share of the satisfied Interests.  
A cross-comparison between Figure 13a and Figure 13b reveals that the caching effect is significantly more evident for the Zipf traffic. The rationale is that this traffic type results in a much wider spread of popular content among the mobile nodes. Remarkably, the difference between enabled and disabled cache is evident already for \(K=1\) for the Zipf traffic model. Similarly, it is easy to observe the reliability and delay gains of Zipf traffic over the CBR traffic.
## 3 Results
### Spatial Patterns of WUE  
The average WUE for the total area in the TNR showed larger spatial variations from the west to the east (Figure 3). The mean WUE (WUE-M) from 1982 to 2015 was 1.40 g C kg\({}^{-1}\) H\({}_{2}\)O and ranged from approximately 0 to 4 g C kg\({}^{-1}\) H\({}_{2}\)O. The WUE of the western regions (regions I and II) was lower, while the WUE in the eastern regions (regions III and IV) was higher. The pixels of the cropland, forest and grassland types that were unchanged in the land cover data from 1990 to 2015 were selected to calculate their WUE values. The results showed that the WUE was significantly different among the croplands, forests and grasslands (one-way ANOVA, \(p<0.001\), Figure 3a-d) in each region. Specifically, the grasslands had the lowest WUE values of the whole TNR, the forests had the highest WUE values in the eastern TNR (regions I and II) and the croplands had the highest WUE values in the arid areas of the western TNR (regions III and IV).
# Hysteresis Compensation and Trajectory Tracking Control Model for Pneumatic Artificial Muscles
## 3 Position Control Strategy of PAM  
During the operation of a PAM, geometric deformations occur, and these nonlinear deformations affect the trajectory tracking control accuracy of the upper limb exoskeleton robot driven by the PAM. In this paper, a feedforward control strategy based on the QUPI inverse model is employed, combined with a composite control strategy that integrates incremental PID-feedback control and incremental PID iterative learning. The target displacement of the PAM serves as the input to the inverse model, and the output pressure is applied to the PAM. The controlled system (FN-QUPI model) then calculates the actual displacement of the PAM. Although the feedforward control using the QUPI inverse model compensates for the hysteresis nonlinearity, bringing the output closer to the actual value, some errors still remain. To address this shortcoming, the QUPI inverse model feedforward control is combined with incremental PID-feedback control. Specifically, the system first provides a feedforward signal based on the reference signal, followed by the incremental PID controller adjusting for system errors. The final input signal to the system is the sum of the feedforward and feedback signals. The output value of the PAM is influenced by the previous output value, so iterative learning is added on top of the incremental PID algorithm. Iterative learning control (ILC), by performing multiple repeated operations, gradually improves the control precision, making it particularly suitable for repetitive tasks. ILC uses the error information from previous operations to adjust the control input for the current operation, thereby continuously approaching the ideal control result and significantly enhancing control accuracy.
## 2 Results and Discussion
### Volatile Compounds in Prepared Green Teas
#### 2.1.3 Composition Based on Geographic Area  
Green teas produced in the Africa region commonly had 1-penten-3-ol, 2-penten-1-ol,linalool, hexanal, and benzaldehyde. These green teas may provide green, floral, fruity and nutty aroma during infusion and consumption. No other information on volatile compounds of green tea from Africa was found.  
Green teas from Southeast Asia and the Indian subcontinent generally had linalool and nonanal and half of the samples had 2-penten-1-ol, hexanal and \(\beta\)-ionone. Based on these common volatile compounds, these green teas may have green, floral, woody and citrus aroma. Information on volatile compounds of green teas from these areas was limited and only 2-Penten-1-ol has been reported from Vietnamese green tea [22]. The composition of 1-pentene-3-ol was 2-4 times higher in the sample from Vietnam than all other samples.  
Green teas from Northeast Asia (China, Japan and Korea), the most common growers of green tea in international trade, typically had linalool, hexanal and nonanal, which will provide green and floral aromas. These compounds have been reported by others [36,43,44] from samples produced in the same region. Additionally, about half of the Chinese green teas had geraniol, benzaldehyde and jasmone. Those compounds previously have been reported from Chinese green tea [34,36,38] can provide floral and nutty aromas. About half of the Japanese green teas also had toluene and \(\beta\)-ionone. Toluene [40] and \(\beta\)-Ionone [12,44] have been reported in teas from Japan. Approximately 65% of Chinese green teas had \(\beta\)-ionone [25]. In this study, \(\beta\)-ionone was most commonly found in Japanese green tea (4 out of the 6 samples). Korean green teas also had benzeneethanol, benzaldehyde, and jasmone, which have been reported previously [7] and may provide floral, fruity, and nutty aromas.  
The parch-processed tea samples in [PERSON]'s study [7] showed higher concentration of linalool when compared with the steam-processed samples, which is in agreement with our study. Linalool concentrations in Chinese and Korean samples generally were higher than Japanese samples. Chinese and Korean green teas are mostly roast-processed while Japanese green teas are steam-processed [13]. For instance _Longjing_ (roast-processed Chinese green tea) contained linalool at much higher concentrations than _Sencha_ (steam-processed Japanese green tea) [11]. China 7, which is a _Longjing_ tea, had higher concentrations (0.55 ng/kg) than the _Sencha_ teas from Japan (Japan 2 & 3 both at 0.27 ng/kg) (Table 3). In previous research _Longjing tea_ had more geraniol than _Sencha tea_[11], which also was true for our study.
# Semi-Supervised Medical Image Classification Combined with Unsupervised Deep Clustering
## 5 Discussion  
We designed a semi-supervised classification model with unsupervised learning and compared it with current semi-supervised learning methods on the ISIC 2018 and ICIAR BACH 2018 datasets. On the ISIC 2018 dataset, we achieved significant improvement in AUC and sensitivity. On the ICIAR BACH 2018 dataset, our method performed better on all metrics, especially in AUC and error rate. The results show that our method has stronger classification ability and can distinguish between positive and negative samples more clearly. We believe that this is the result of the combined action of semi-supervised and unsupervised classification mechanisms. When the proportion of labeled data decreases, the advantage of our method becomes more obvious. This shows that intermittently ignoring label information during neural network training can effectively avoid overfitting of the model. We improve the effect of unsupervised clustering by cross-comparison and auxiliary overclustering during unsupervised learning, ultimately improving the performance of the entire model. Considering the high initialization requirement of clustering algorithms, we use a network pre-trained on ImageNet as the feature extraction network. The results of random initialization in our experiments are not ideal, which is a problem that the deep clustering framework needs to address. In future studies, we will try to improve the performance of the model under random initialization and make it have stronger generalization ability.  
Semi-supervised learning has great potential in medical image classification. Labeling medical images is often time-consuming and expensive, so semi-supervised learning can leverage large amounts of unlabeled data to improve model accuracy. In reality, a large number of unlabeled medical images are generated every day, making it practical to learn from these data in an unsupervised way. However, current semi-supervised learning methods still have some limitations, such as not adequately addressing the problem of class imbalance or being prone to overfitting when there is insufficient labeled data. We have shown that unsupervised clustering can be combined with semi-supervised learning to improve overall performance. Our work focuses on extracting meaningful semantic information from a large number of unlabeled data and dividing the same category into the same cluster to help the semi-supervised model classify better. In general, our method can be considered a supplementary means of semi-supervised learning, and we hope to reduce the model's dependence on manually labeled data.
## 6 Application to Experimental Data  
In this section, the proposed approach is verified through application to real data. The experimental data set used in this application is Pond Experiment 2010 (PondEx10), which was carried out by the Applied Physics Laboratory in the University of Washington (APL-UW), Seattle, WA, USA for identifying and understanding target and environmental factors affecting sonar performance and identifying robust signal characteristics unique to a given underwater unexploded ordnance (UXO) [25; 32]. In this application, first, the logistic regression model is trained using the simulated data presented by the method mentioned in Section 3, and the experimental measurements are used to test the trained model. Since the data set considered in this study consists of the backscattering measurements for an aluminum cylinder of 61 cm in length and 30.5 cm in diameter, the simulated data are generated according to this size. The performance is measured in terms of the Receiver Operating Characteristic (ROC) curve and the confusion matrix.
## 1 Introduction
In response to the challenges in AIS data processing and the limitations of current ship trajectory prediction models, this paper makes the following three main contributions:  
Addressing the real-time requirements for trajectory prediction with AIS data, we propose an AIS trajectory data preprocessing method. Our method includes trajectory segmentation, removal of anomalous/redundant points, patching missing points, and resampling at equal time intervals. It is capable of handling large volumes of AIS data, generating equidistant and complete ship trajectories, meeting the input requirements of trajectory prediction models. To overcome the shortcomings of recurrent neural networks in ship trajectory prediction, we introduce a ship trajectory prediction method based on the TRFM (Transformer) model. Leveraging the multi-head attention mechanism of the TRFM model, it adapts to focus on features throughout the sequence and assigns higher weights to crucial trajectory points. By extracting global temporal feature information from the trajectory sequence based on attention weights, it addresses the issue of traditional recurrent neural networks primarily attending to the tail-end features of the sequence and overlooking global sequence characteristics. Additionally, the TRFM model performs parallel computation when processing sequence features, enhancing the efficiency of trajectory prediction. To ensure consistent trajectory prediction performance across different latitude and longitude regions and enhance model generalization, we propose a trajectory prediction data structure where the input and output sequences are based on the relative values of latitude and longitude. Differential processing is applied to adjacent trajectory points' latitude and longitude, utilizing the heading angle, velocity, relative longitude, and relative latitude as input sequence information, and relative longitude and relative latitude as output sequences. The final latitude and longitude positions of the trajectory sequence are recorded for regressing the absolute latitude and longitude values. This approach improves model generalization. The remaining sections of this paper are organized as follows: Section 2 focuses on the detailed aspects of AIS data processing and the design of the TRFM model structure. Section 3 presents experimental comparative analyses. Section 4 provides the conclusion and Section 5 presents the limitations and future work.
## 3 Micro-Vibration Test Principle
### The Image Point Offset Extraction and LOS Jitter Calculation of the HPSOP
#### 3.2.1 Image Point Offset Extraction  
As shown in Figure 5a, during the micro-vibration test, the image point of the dot target imaged by the optical system will shift on the CCD. Therefore, we plan to evaluate the effect of micro-vibration on the optical system based on the offset of the image point. However, affected by the micro-vibration and non-ideal optical system, the image usually appears to be a light spot rather than an ideal point, as shown in Figure 5b.  
Typically, to reflect the overall offset of the light spot, the grayscale centroid method is usually used to calculate the energy center position of the light spot. As shown in Equation (4), the position of the pixel with the maximum grayscale value is taken as the center, and the grayscale subdivision is performed within an n \(\times\) n window. This approach allows the measurement accuracy to reach the sub-pixel level.  
\[\left\{\begin{array}{l}u_{cm}=\frac{\sum_{j=1}^{16}\sum_{i=1}^{16}G_{ij}^{m} -u_{j}}{\sum_{j=1}^{16}\sum_{i=1}^{16}G_{ij}^{m}}\\ v_{cm}=\frac{\sum_{j=1}^{16}\sum_{i=1}^{16}G_{ij}^{m}-v_{j}}{\sum_{j=1}^{16} \sum_{i=1}^{16}G_{ij}^{m}}\end{array}\right. \tag{4}\]  
where \(u_{cm}\) and \(v_{cm}\) express the gray value centroid of the \(m\)-th frame image along the U and V coordinates, respectively; \(G_{ij}^{m}\) represents the gray value of the pixel at the \(i\)-th row and \(j\)-th column; and \(u_{i}\) and \(v_{i}\) indicate the energy center of the pixel at the \(i\)-th row and \(j\)-th column.  
However, the image point extracted by Equation (4) is not only determined by the micro-vibration but also influenced by the electronic noise caused by the CCD [5]. To solve this problem, an \(N\)-th power-weighted grayscale value solution method with a threshold \(T_{h}\) is proposed to reduce the grayscale value error, as shown below:  
\[\left\{\begin{array}{l}u_{cm}^{\prime}=\frac{\sum_{j=1}^{16}\sum_{i=1}^{16} \left(G_{ij}^{m}-T_{h}\right)^{N}-u_{j}}{\sum_{j=1}^{16}\sum_{i=1}^{16}G_{ij}^ {m}}\\ \\ v_{cm}^{\prime}=\frac{\sum_{j=1}^{16}\sum_{i=1}^{16}\left(G_{ij}^{m}-T_{h} \right)^{N}-v_{j}}{\sum_{j=1}^{16}\sum_{i=1}^{16}G_{ij}^{m}}\end{array}\right. \tag{5}\]  
where \(T_{h}=k\max(G_{ij}^{m})\), and \(k\) indicates the scaling factor changing with the image quality (0 \(<\)\(k\)\(<\) 1). Studies have shown that when \(T_{h}\) is the background gray and \(N\) is 2, the improved centroid accuracy has an extreme point.  
Figure 5: Image point motion caused by the micro-vibration. (**a**) Sampling results at different times. (**b**) Single-frame enlarged view.
## 4 Engineering Applications and Discussion
### Monitoring Equipment and Layout Method  
Digital convergence gauge JSS30A is used to monitor the cross-section displacement of the roadway. This equipment is manufactured by Shanghai LeiYun Testing Instruments Co., Ltd., located in Shanghai, China. The equipment installation method is shown in Figure 6. Two convergence gauges are connected to three monitoring points, A, B, and C, within the roadway. Relative displacements \(\Delta\)AB and \(\Delta\)BC are recorded and displayed on the digital convergence gauges to calculate roof displacement \(\Delta\)CD when the distances between monitoring points (\(L_{\text{AB}}\) and \(L_{\text{BC}}\)) change. This monitoring method accurately tracks the displacement changes of the roadway's cross-section, which obtains the relative displacements between two points at different times. The anchoring bolts of the digital convergence gauges are drilled and fixed before the application of shotcrete. After that, the digital convergence gauges are installed to read data at intervals of 3-7 days.
## 1 Introduction
### Construction of Demagnetization Fault Indications  
#### 2.1.1 Placement of Toroidal-Yoke-Search Coil  
The placement of sensors to obtain fault signatures is the most significant step in the fault diagnosis architecture. The placement of the search coil for obtaining the demagnetization fault information is shown in Figure 1. Three additional toroidal-yoke coils (TYC1, TYC2, and TYC3) are wound around the stator yoke to measure the stator yoke flux at  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**References** & **Methods** & **Advantages** & **Limitations** \\ \hline
[17] & SVM-based method & effective for small sample data, strong generalization ability & low training efficiency for large-scale data, multiclassification problems are complex to handle, difficulties in kernel function selection and parameter adjustment \\ \hline
[18] & KNN-based method & no training process (non-parametric model), able to handle complex data distributions & high computational complexity, sensitive to the curse of the dimensionality of data, sensitive to the distribution and proportion of data \\ \hline
[19; 20; 21] & ANN-based method & strong learning ability, parallel processing capabilities, and a certain tolerance for noise and partially missing data in the data & requires a large amount of data and computing resources, prone to overfitting, difficulties in network structure and parameter selection \\ \hline
[22; 23] & PNN-based method & learning process is simple, high classification accuracy, insensitive to outliers & need to determine the smoothing coefficient \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of artificial intelligence-based fault diagnosis methods for PMSMs.  
different positions of the stator, and the distance between adjacent toroidal-yoke coils is approximately polar distance to strengthen fault demagnetization characteristics.
## 3 Materials and Methods
### Methods
#### 3.2.2 Spatial Autocorrelation Analysis  
Spatial autocorrelation is the term used to describe the dependency and covariance of variables within a geographic area, and it reveals the phenomenon of geographic proximity's potential [74; 75]. In this paper, both global and local measures of spatial autocorrelation were calculated using the Python package GeoRasters to evaluate the degree of spatial autocorrelation in vegetation growth patterns across the study region and to indicate the variations in spatial autocorrelation throughout the study period [12; 76].  
[PERSON]'s I [77] is one of the most commonly used indexes of global spatial autocorrelation. [PERSON]'s I values range from \(-1\) to \(1\): the index value from \(0\) to \(1\) indicates a positive autocorrelation, which implies a clustering state of spatial geographical phenomena, and the index value from \(-1\) to \(0\) represents a negative autocorrelation, which implies a larger difference between neighbors, [74; 78; 79; 80]. The formula for [PERSON]'s I is given in Equation (2):  
\[I=\frac{n\sum\limits_{i=1}^{n}\sum\limits_{j=1}^{n}w_{ij}(x_{i}-\overline{x}) \big{(}x_{j}-\overline{x}\big{)}}{\sum\limits_{i=1}^{n}\sum\limits_{j=1}^{n}w _{ij}\sum\limits_{i=1}^{n}\big{(}x_{i}-\overline{x}\big{)}^{2}} \tag{2}\]  
where \(x_{i}\) and \(x_{j}\) are the values of variable \(x\) (vegetation indexes) at location \(i\) and location \(j\), respectively. \(\overline{x}\) is the average value of \(x\), \(n\) is the total number of locations, and \(w_{ij}\) represents the spatial weight [4; 44; 81; 82].  
A local spatial autocorrelation analysis was performed using the local indicators of spatial association (LISA) [83; 84] to indicate the spatial heterogeneity of the geographic phenomena at the local level across the study area, based on the following equation [74; 83; 85]:  
\[I_{\text{i}}=\frac{(x_{i}-\overline{x})}{S^{2}}\sum\limits_{j}w_{ij}(x_{j}- \overline{x}) \tag{3}\]  
where \(S^{2}\) represents the variance of the observed values. In this study, the LISA map shows the spatial distribution (clustered/random/dispersed) of each vegetation index based on 9999 permutations at the significance level of \(p<0.05\). The spatial cluster/outlier characteristics of regions can be divided into four categories: low-low clusters (LL), high-high clusters (HH), low-high outliers (LH), and high-low outliers (HL). Positive and negative LISA values represent the aggregation of similar observations--\"spatial cluster\", and different observations--\"spatial outlier\", respectively [79; 80; 86; 87; 88; 89].