esa-sceva/satcom-chunk-collection huggingface dataset:
# A Deep Curriculum Learning Semi-Supervised Framework for Remote Sensing Scene Classification
## 2 Related Work  
### Semi-Supervised Learning  
Over the past decade, semi-supervised learning (SSL) has been widely applied in many fields [8; 9; 10; 11; 12]. The idea of semi-supervised learning is to make full use of unlabeled data to learn during the model learning phase, thus improving the performance of the model.  
In fact, there are many unlabeled samples in a large number of images of remote sensing scenes, so many researchers use semi-supervised classification methods to improve the classification ability of models in remote sensing scenes [8; 9; 10]. [PERSON] et al. [8] proposed semi-supervised representation consistency Siamese network (SS-RCSN) for remote sensing image scene classification. The image features are extracted through the GAN network, and then the differences between labeled and unlabeled samples are learned by the Siamese network, and their differences are reduced by representing the consistency loss function. The network has achieved excellent results on publicly available datasets. [PERSON] et al. [9] proposed the MixMatch algorithm, which generates a pseudo-label for each unlabeled sample by combining entropy minimization and consistency regularization into a loss function. [PERSON] et al. [10] proposed the FixMatch method, which uses the model to learn from two types of unlabeled images, weak enhancement and strong enhancement, and then generates pseudo-labels.  
While these semi-supervised methods achieve good classification performance, there are still several problems. First, most of these methods are single networks, limiting the ability to extract features. Second, these methods typically require high computing resources and a lot of time to train the model. In order to reduce the training time of CNNs, pretrained networks are usually used to extract image features.
# Intent to Adopt Location Sharing for Logging Safety Applications
## 1 Introduction
The last factor, control, was originally defined by [PERSON] (1991) as either self-efficacy or perceived behavioral control, which he deemed as interchangeable concepts [27]. Consequent analyses and discussions have distinguished between the internal factors that influence self-efficacy (i.e., difficulty) and the external forces of perceived behavioral control (i.e., how much the behavior is up to the individual) [28; 31]. The TPB has been used widely to understand and explain variance in behavior [31], especially for health and safety [32; 33; 34; 35]. It has also been employed as a framework for questionnaires addressing occupational health behaviors, guiding surveys on safe lifting [36], farmers' self-protective behaviors [37], miners' use of hearing protection [38], and safety violations by aircraft maintenance workers [39]. We surveyed Idaho loggers at three accreditation renewal workshops as part of the Logger Education to Advance Professionalism (LEAP) program. Our objectives were to evaluate whether Idaho loggers are likely to adopt LS-based safety practices on logging operations and to elucidate the nuances associated with adoption of these behaviors. The survey encompassed three general themes: (a) background information on participants (e.g., demographic data, logging experience, smartphone ownership and use, prior use of LS, and general safety opinions), (b) formal TPB assessment of intent, attitude, descriptive norms, and perceived behavioral control, formatted according to suggested guidelines, and (c) additional LS adoption elements. This latter component included, for example, possible ways that LS might be used and potential reasons for disuse. Within these broader topics, we tested specific hypotheses to assess (1) whether loggers believe that finding new ways to improve safety on logging operations is important, (2) whether loggers are likely to use LS, and (3) whether intent to adopt LS behaviors varies by age, phone use, or the TPB attributes (attitude, norms, and control).
## 3 Case Study and Results Analysis
### Case Study 1
Thirdly, in the FA-w-SVM model, the _Max_RE and _Min_RE values are 5.91% and 1.02%, respectively, both of which are smaller than that of PSO-SVM model(6.75% and 1.82%), which means FA-w-SVM has better forecasting accuracy. Finally, both the _Max_RE and _Min_RE values of SVM are 8.73% and 2.73%, respectively, both of  
Figure 8: Forecasting errors of each model. which are smaller than that of MLR, that mean the nonlinear processing ability is better than that of MLR; in other words, the robustness of SVM is much stronger than that of the MLR model. The MAPE, MSE and AAE values of the above mentioned five models are shown in Figure 9. The experimental results show that the forecasting effect of the SVM model obviously precedes the multiple linear regression model; this illustrates that the intelligent forecasting model has strong learning ability and nonlinear mapping ability which the multiple linear regression model cannot match. In addition, the MAPE value of single SVM is 4.988%, which is much higher than that obtained by hybrid QFA-w-SVM, FA-w-SVM and PSO-SVM models (which are 1.56%, 2.83% and 2.776%, respectively); this result proves that the optimization algorithm can help single SVM to find the optimal parameters, and heighten the learning capacity and forecasting accuracy. Furthermore, the MAPEs of QFA-w-SVM and FA-w-SVM are 1.56% and 2.83%, respectively; this shows that the QFA model can greatly improve the optimization performance of regular FA model through the combination of quantum optimization algorithm, which makes the algorithm can easily get the optimal results. Meanwhile, the MAPE value of PSO-SVM model is 2.776%, which is higher than that of QFA-w-SVM; this illustrates that QFA-w-SVM model has a better global convergence performance compared with PSO-SVM, and also proves that w-SVM has better nonlinear mapping capability compared with SVM. Furthermore, the MSE values of QFA-w-SVM, FA-w-SVM, PSO-SVM, SVM and MLR models are 0.0174, 0.0466, 0.0590, 0.1094 and 0.3581, respectively, and the AAE values of those five models are 0.024, 0.0389, 0.0439, 0.0602 and 0.1092, respectively. As we know, when the value of MSE and AAE is smaller, the prediction effect of model is more ideal. It can be clearly seen that the MSE value and AAE value of QFA-w-SVM is the smallest among other models, which directly demonstrates the feasibility and effectiveness of QFA-w-SVM model, and QFA can improve the optimization performance of the regular FA to help SVM find the optimal parameters to improve the forecasting accuracy.
# The Use of Earth Observation Data for Railway Infrastructure Monitoring--A Review
## 3 EO-Based Railway Infrastructure Monitoring: Literature Review  
### Comparative Analysis of EO Data Use in Railway Infrastructure Monitoring  
The analyzed papers are summarized in Table 1, based on their use case, satellite constellation used, and data used from the constellation. Furthermore, the analysis is performed with respect to whether the papers report only the possibilities for the evaluation, they report on the performed evaluation by testing, or the EO-based solution is compared to conventional approaches as validation of the proposed solution. In the latter case, the venue of the evaluation and/or testing and validation is analyzed. Following the summary given in Table 1, examples of EO-based applications are described in more detail in the same order of use cases as given in Section 2.
###### Abstract  
According to statistics from past earthquakes, it is observed that multi-story reinforced concrete (RC) frames represent a large proportion of the structural failures or collapses in seismic events. Hence, research on seismic collapse mechanisms and risks of RC frame structures subjected to extreme earthquakes is of foremost importance. Both experimental and numerical studies have been substantially carried out in this field. In order to represent an actual process of structural damage in an actual seismic event and provide a calibration test for numerical studies, a shake table collapse test of a typical multi-story RC frame structural model, which is scaled from a nearly collapsed building in the 2010 Ms 7.1 Yushu earthquake in China, was performed. Both the test and earthquake field investigation indicate that severe damage mainly occurred at the column ends. As dual structural systems, i.e., systems combining frames and additional members that mainly carry seismic loading, could be a better way to solve the unexpected damage mechanism of RC frames, a practical stiffness iteration design method based on the nonlinear static analysis to obtain the optimal stiffness demanding of the lateral load-resisting members in each story is proposed. This approach aims to control the structural deformation pattern along the height. The outcome of this study provides some intrinsic understanding of the inherent collapse mechanisms of similar RC frames during strong earthquakes. It also offers a practical design method to improve the seismic collapse resistance of RC frames.  
Keywords:reinforced concrete (RC) frames; structural collapse; seismic design; failure mode; shake table test +
Footnote †: journal: Applied Sciences
# Cyclostationary Crosstalk Cancelation in High-Speed Transmission Lines
## 2 Linear Periodic Time-Varying Filtering  
Linear time-varying (LTV) systems are described by a wider class of signal processing algorithms than linear time invariant (LTI) systems. The filtering, modulation, compensation, equalization, and compression is representing application examples of LTV systems [16]. The superposition principle of the LTV system is very useful for statistical signal processing. The correlation analysis for LTV filtering of the additive mixture of signals, which contains a desired signal, interferences and noise, can be simplified due to the linear combination of partial output signals.  
The LTV system with periodic impulse response (IR) can be used for realization of the efficient cyclostationary signal estimator known as the cyclic Wiener filter [17] (pp. 240-266). In Section 2.1, the theory of discrete-time LPTV filtering is introduced. In Section 2.2, the synthesis and analysis of the cyclic Wiener filter are discussed.
# Lexicon-Enhanced Attention Network Based on Text Representation for Sentiment Classification
## 4 Experiments
### Baseline Methods  
To comprehensively measure the effectiveness of our proposed models, we compare the LAN and H-LAN models with several strong baseline methods used in the task of text sentiment classification, including conventional machine learning method, popular deep learning methods, deep learning methods based on specified parsing structure, deep neural networks relying on attention mechanism, and hierarchical neural networks.  
* SVM [24]: A classic support vector machine classifier applied in text sentiment classification, which is trained with the following groups of features: n-grams, lexicon features, and part-of-speech tags.
* CNN [29]: Convolutional neural network consists of multiple one-dimensional convolution filters and a max pooling layer. This structure can capture the local features inside a multi-dimensional field to generate context-aware text representation.
* LSTM [32]: The standard recurrent neural network based on long short-term memory unit uses the last hidden state as the representation of the whole text, which can extract sequential correlation information in the raw text.
* CNN-LSTM [31]: A deep neural network combines kernel from multiple branches of CNN with LSTM, which can extract local features within the raw text and sequence information across the raw text.
* Tree-LSTM [34]: Tree-structured long short-term memory network utilizes pre-defined syntax tree structure as external knowledge to guide the training of LSTM network. It can better learn the representation of text from the syntax perspective.
* LR-Bi-LSTM [35]: Linguistically regularized-based bidirectional long short-term memory network is similar to Tree-LSTM, except that linguistic regularization is used as external knowledge to the intermediate outputs of bidirectional LSTM network with KL divergence.  
* LE-LSTM [17]: Lexicon-enhanced LSTM network which uses sentiment lexicon as an extra information to train a sentiment-based word embedding and then combines the sentiment embedding with its word embedding to make word representation more accurate.
* LSTM-GRNN [49]: Hierarchical neural network which consists of a LSTM-based word encoder and a GRU-based sentence encoder for document-level sentiment classification task.
* Self-attention [50]: A self-attention mechanism and a special regularization term are integrated into deep neural networks to construct sentence embedding for sentiment classification task.
* HAN [37]: Hierarchical attention network for document-level sentiment classification which progressively builds a document vector aggregating important words into a sentence and then aggregating important sentences vector into document vectors.
# Automatic Detection of Ballast Unevenness Using Deep Neural Network
## 2 Algorithm for Automatic Detection of Unevenness in Ballast
### Detection of Sleepers
#### 2.1.1 Mask R-CNN Network  
The Mask R-CNN is the network that performs instance segmentation. Figure 5 shows the structure of the Mask R-CNN network. This network uses a convolutional neural network (CNN) to extract feature maps that are used to describe the objects being detected (sleepers). The CNN is often called the \"backbone\", which is usually a pre-trained neural network. At present, three types of convolutional neural networks--AlexNet, VGG-Net and ResNet--can be used as a backbone. AlexNet, as the oldest network type, which produces the largest classification error on the ImageNet database (16.5%) [19], was discarded. VGG-Net reduced the classification error to 7.6% while increasing the number of network layers to 16-19 [29]. Thanks to the adoption of the core idea of Identity Shortcut Connection [30], ResNet further reduces this error to 3.6%. Using classification error on the ImageNet test database as a criterion, the authors decided to test Resnet51 and Resnet101 as a backbone. In order to narrow down the search for an object in an image, the network can be divided into two parts. In the first part, the region of interest (ROI) is determined using the Region Proposal Network (RPN)--marked by red rectangles on the feature map. To generate the ROI, the input of an \(n\times n\) spatial window is slid over the convolutional feature map output, and the features covered by the spatial window are fed into two sibling fully connected layers--a box classification layer and a box regression layer. A box classification layer determines the probability of occurrence of the objects in the ROI area. On the other hand, a box regression layer generates the coordinates of the ROI center and its width and height.  
The size of the ROI area generated by the RPN is variable. For this reason, in the next step (part) of the algorithm, the variable ROI size is converted into a fixed size. Here, fully connected layers also occur--box classification layers and box regression layers--which generate the class of the detected object and the coordinates of the bounding box, respectively. Additionally, the ROI is also fed to a fully convolutional network (FCN) [31] to generate the mask of an object being detected. According to [32], the algorithm uses the following objective function during training:  
\[L=L_{CLS1}+L_{CLS2}+L_{box1}~{}+L_{box2}~{}+L_{mask} \tag{1}\]  
where \(L_{CLS1}\) is a binary classifier loss corresponding to the classes block in RPN; \(L_{box1}\) is a regression loss corresponding to the boxes block in RPN; \(L_{CLS2}\) is a loss for the classifier corresponding to the classes block in the box head; \(L_{box2}\) is a regression loss corresponding to the boxes block in the box head; and \(L_{mask}\) is a mask binary cross-entropy loss corresponding to the mask block in the mask head.
# Numerical Study on the Route of Flame-Induced Thermoacoustic Instability in a Rijke Burner
## 2 Numerical Model  
### Rijke Burner Configuration  
The Rijke burner used in this study consists of a 1572 mm-long steel tube with an inner diameter of 73.7 mm, as shown in Figure 1. A premixed methane-air mixture is fed into the inlet end of the tube, and a honeycomb flame stabilizer with a thickness of 25.4 mm is positioned in the middle of the tube. A flat flame is anchored at the top of the honeycomb flame stabilizer, and releases unsteady heat into the flow, inducing pressure oscillation in the tube. A 2-D axisymmetric flow field is used to approximate the 3-D flow field to simplify the computational model as shown in Figure 2.  
The model of the Rijke burner is generated, and boundary conditions are set, including the mass flow rate inlet, pressure outlet, coupled wall between the honeycomb and flow, coupled wall between the inside tube wall and flow, outer tube walls, and axisymmetric centerline. The input parameters used in setting up the Rijke burner mode are as follows. The incoming flow is set to 1.27 \(\times\) 10\({}^{-4}\) kg/s with a fuel/air equivalence ratio of 0.75. The equivalence ratio is defined as the value of the actual fuel/air mass ratio relative to stoichiometric fuel/air ratio. If it is less than 1, the combustion is lean with excess air. The convective heat transfer coefficient for the outside tube wall is estimated to be 20 \(W\)/(m\({}^{2}\cdot\)K), and the reference condition is set as normal temperature and pressure. The boundary conditions and initial conditions are summarized in Tables A1 and A2 of Appendix A, respectively.  
Figure 1: Schematic of the Rijke burner and the honeycomb flame stabilizer (not to scale).  
Figure 2: Configuration of the axisymmetric Rijke burner model (not to scale).
## 3 Theoretical Framework
### Wind Turbines and DFIG Systems
In some systems, Equations (1) to (4) can be written according to the linkage flux instead of the flux in the per-unit system:  
Figure 2:7 Figure 2:8 (15) Figure 2:9 (16) \[V_{qs}=R_{s}i_{qs}+\omega_{e}\lambda_{ds}+\frac{d}{dt}\lambda_{qs}, \tag{1}\]0  
where \(\psi\) denotes the linkage flux, and \(\omega_{b}\) denotes the base angular speed. In this solution, the rotor side converter feeds a voltage with variable amplitude and frequency to the rotor's winding (Figure 1). In this way, the DFIG system is entirely controllable using the converter. The grid side converter and the ANFIS control the DC link voltage and reactive power. The schematic diagrams, all equations, and a discussion of how these two converters control the parameters of the DFIG and grid are presented in [53]. Regarding the robustness of ANFIS and DFIG systems in wind turbines, refer to Refs. [55; 56], which provide valuable insights. The modeled wind farm is a 9 MW wind farm comprising six 1.5 MW wind turbines connected to a 25 kV distribution system that exports power to a 120 kV grid through a 30 km, 25 kV feeder. The stator winding is connected directly to the grid, while the rotor is fed at variable frequency through the AC/DC/AC converter. The DFIG technology, combined with ANFIS, allows extracting maximum energy from the wind for low wind speeds by optimizing the turbine speed while minimizing turbine mechanical stresses during wind gusts [57]. The maximum and minimum wind speeds in the ANFIS design were assumed as 60 m/s and 4 m/s, respectively. Other implementation features of the two rotor side and grid side converters were defined based on the suggestions presented in [54; 58]. Figure 3 depicts the modeled wind farm and the grid, including its transmission line, load, and transformers [54; 58].
## 3 Numerical Results and Discussion
### The Influence of Sprung Eccentricity
* Due to the static sprung eccentricity, the center for the orbit of support I would be offset from the center of the SFD and thus make the SFD ineffective or even lead to the occurrence of rubbing impact, as shown in Figure 15a. Figure 15 presents the orbit and the spectrum of support I with \(e_{2}=0.1\) mm, \(e_{xx}=0.35\) and \(\omega_{1}=3600\) rad/s. It can be seen from Figure 15a,b that the amplitude of the orbit is much smaller than the static runout there, which means that the oil film was barely squeezed. In addition, 2X and 3X components can be observed in the spectrum. Figure 14: Horizontal response of the rotor system in \(1000\!-\!4500\) rad/s with \(e_{2}=0.1\) mm, \(e_{xx}=0.35\): (a) 0X; (b) 1X. Figure 7:0 Orbit and spectrum of support I with \(e_{2}=0.1\) mm, \(e_{xx}=0.35\) and \(\omega_{1}=3600\) rad/s: (a) orbit; (b) spectrum.
## 3 Results
### The PM\({}_{2.5}\) of Satellite Estimation Validation  
The comparison of Figures 4 and 5 shows that the correlation between \(\sigma_{\rm dry}\) and PM\({}_{2.5}\) is significantly improved after the vertical and humidity corrections compared to that between AOD and PM\({}_{2.5}\), and the correlation coefficient increases from 0.11-0.44 to 0.28-0.68. The mass concentration of near-ground PM\({}_{2.5}\) was estimated using Equation (8), and the hourly-hour density scatter plots of satellite estimates against ground-based observations are shown in Figure 6, where the red dashed line is the 1:1 line, and the blue dashed line is the fitted line. According to Figure 6 the correlations are all high in general, but the correlation coefficient varies more from 09:00 to 16:00 (0.69-0.85), and the correlation is significantly higher in the afternoon than in the morning, the variation of which is consistent with the study of [PERSON] [30]. This may be caused by the following two reasons: (1) in the morning hours, when the solar azimuth is larger and the atmospheric water vapor content is higher, the sunlight passes through a longer atmospheric path to reach the ground, which affects the accuracy of the AOD retrieval, and thus the PM\({}_{2.5}\) estimation results may be relatively poor, especially at 09:00. (2) Particulate matter deliquesces with the increase in temperature, which may affect the results of humidity corrections [15] and thus affect the PM\({}_{2.5}\) estimation results. (3) In the process of vertical revision, we use the visibility to calculate the near-surface extinction coefficient (\(\sigma_{a}(\lambda)\)). The fog or high humidity conditions in the morning over the Chengdu-Chongqing regions may affect the visibility measurement, thus affecting the final PM\({}_{2.5}\) estimation [51]. (4) We fit this based on the monthly scale in constructing the hygroscopic correction factor grid, which may have a greater impact at 9:00 and 10:00 because of the high humidity conditions, so it is necessary to construct the hygroscopic correction factor grid for a more refined sub-hour.
# Resource Allocation in C-V2X Mode 3 Based on the Exchanged Preference Profiles
## 3 Problem Formulation
### \(\alpha\)-Fair Utility Function  
To improve the resource allocation fairness among CUEs, we introduce the \(\alpha\)-fair utility function [29], which is defined as  
\[U_{\alpha}(x)=\left\{\begin{array}{ll}\log(x),&\alpha=1\\ x^{1-\alpha}/(1-\alpha),&0\leq\alpha<1\end{array}\right. \tag{10}\]  
where the parameter \(\alpha\in[0,1]\) and characterizes the trade-off between the allocation efficiency and fairness.  
As observed in (10), the \(\alpha\)-fair function is a nondecreasing concave-down function, the slope of which decreases as \(x\) increases. Thus, for the \(\alpha\)-fair function, the increases in low rates are more favored than the larger rates. For example, an RB is scheduled to be assigned to \(\text{CUE}_{1}\) or \(\text{CUE}_{2}\), the current transmission capacities of which are \(x_{1}\) and \(x_{2}\), respectively. Since the derivatives \({U_{\alpha}}^{\prime}(x_{1})\) and \({U_{\alpha}}^{\prime}(x_{2})\) of the utility function satisfy \({U_{\alpha}}^{\prime}(x_{1})>{U_{\alpha}}^{\prime}(x_{2})\) for \(x_{1}<x_{2}\), the RB allocated to \(\text{CUE}_{1}\) will obtain more system utility values, achieving allocation fairness between \(\text{CUE}_{1}\) and \(\text{CUE}_{2}\). Moreover, the larger the value of \(\alpha\), the better the fairness of the resource allocation results.